Award type：Award from Japanese society, conference, symposium, etc.  Country：Japan

Award type：Award from Japanese society, conference, symposium, etc.  Country：Japan

Award type：Award from Japanese society, conference, symposium, etc. 

Award type：Honored in official journal of a scientific society, scientific journal 

Award type：Award from Japanese society, conference, symposium, etc. 

Award type：Award from Japanese society, conference, symposium, etc. 

Award type：Award from Japanese society, conference, symposium, etc. 

Award type：Award from Japanese society, conference, symposium, etc. 

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.clinph.2022.04.012

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PubMed

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

Although medical checkup data would be useful for identifying unknown factors of disease progression, a causal relationship between checkup items should be taken into account for precise analysis. Missing values in medical checkup data must be appropriately imputed because checkup items vary from person to person, and items that have not been tested include missing values. In addition, the patients with target diseases or disorders are small in comparison with the total number of persons recorded in the data, which means medical checkup data is an imbalanced data analysis. We propose a new method for analyzing the causal relationship in medical checkup data to discover disease progression factors based on a linear non-Gaussian acyclic model (LiNGAM), a machine learning technique for causal inference. In the proposed method, specific regression coefficients calculated through LiNGAM were compared to estimate the causal strength of the checkup items on disease progression, which is referred to as LiNGAM-beta. We also propose an analysis framework consisting of LiNGAM-beta, collaborative filtering (CF), and a sampling approach for causal inference of medical checkup data. CF and the sampling approach are useful for missing value imputation and balancing of the data distribution. We applied the proposed analysis framework to medical checkup data for identifying factors of Nonalcoholic fatty liver disease (NAFLD) development. The checkup items related to metabolic syndrome and age showed high causal effects on NAFLD severity. The level of blood urea nitrogen (BUN) would have a negative effect on NAFLD severity. Snoring frequency, which is associated with obstructive sleep apnea, affected NAFLD severity, particularly in the male group. Sleep duration also affected NAFLD severity in persons over fifty years old. These analysis results are consistent with previous reports about the causes of NAFLD; for example, NAFLD and metabolic syndrome are mutual and bi-directionally related, and BUN has a negative effect on NAFLD progression. Thus, our analysis result is plausible. The proposed analysis framework including LiNGAM-beta can be applied to various medical checkup data and will contribute to discovering unknown disease factors.

DOI： 10.1016/j.artmed.2022.102310

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PubMed

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.ifacol.2022.04.224

Web of Science

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：一般社団法人 人工知能学会  

DOI： 10.11517/jjsai.36.6_674

CiNii Research

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.vascn.2021.107127

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PubMed

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

<title>Abstract</title><sec>
<title>Purpose</title>
Sleep apnea syndrome (SAS) is a prevalent sleep disorder in which apnea and hypopnea occur frequently during sleep and result in increase of the risk of lifestyle-related disease development as well as daytime sleepiness. Although SAS is a common sleep disorder, most patients remain undiagnosed because the gold standard test polysomnography (PSG), is high-cost and unavailable in many hospitals. Thus, an SAS screening system that can be used easily at home is needed.


</sec><sec>
<title>Methods</title>
Apnea during sleep affects changes in the autonomic nervous function, which causes fluctuation of the heart rate. In this study, we propose a new SAS screening method that combines heart rate measurement and long short-term memory (LSTM) which is a type of recurrent neural network (RNN). We analyzed the data of intervals between adjacent R waves (R-R interval; RRI) on the electrocardiogram (ECG) records, and used an LSTM model whose inputs are the RRI data is trained to discriminate the respiratory condition during sleep.


</sec><sec>
<title>Results</title>
The application of the proposed method to clinical data showed that it distinguished between patients with moderate-to-severe SAS with a sensitivity of 100% and specificity of 100%, results which are superior to any other existing SAS screening methods.


</sec><sec>
<title>Conclusion</title>
Since the RRI data can be easily measured by means of wearable heart rate sensors, our method may prove to be useful as an SAS screening system at home.


</sec>

DOI： 10.1007/s11325-020-02249-0

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PubMed

Other Link： http://link.springer.com/article/10.1007/s11325-020-02249-0/fulltext.html

Language：English   Publishing type：Research paper (scientific journal)  

Web of Science

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.ifacol.2021.10.310

Web of Science

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.3389/fneur.2020.567984

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PubMed

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Institute of Electrical and Electronics Engineers (IEEE)  

DOI： 10.1109/tmrb.2020.3020687

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

© Copyright © 2020 Fujiwara, Huang, Hori, Nishioji, Kobayashi, Kamaguchi and Kano. A considerable amount of health record (HR) data has been stored due to recent advances in the digitalization of medical systems. However, it is not always easy to analyze HR data, particularly when the number of persons with a target disease is too small in comparison with the population. This situation is called the imbalanced data problem. Over-sampling and under-sampling are two approaches for redressing an imbalance between minority and majority examples, which can be combined into ensemble algorithms. However, these approaches do not function when the absolute number of minority examples is small, which is called the extremely imbalanced and small minority (EISM) data problem. The present work proposes a new algorithm called boosting combined with heuristic under-sampling and distribution-based sampling (HUSDOS-Boost) to solve the EISM data problem. To make an artificially balanced dataset from the original imbalanced datasets, HUSDOS-Boost uses both under-sampling and over-sampling to eliminate redundant majority examples based on prior boosting results and to generate artificial minority examples by following the minority class distribution. The performance and characteristics of HUSDOS-Boost were evaluated through application to eight imbalanced datasets. In addition, the algorithm was applied to original clinical HR data to detect patients with stomach cancer. These results showed that HUSDOS-Boost outperformed current imbalanced data handling methods, particularly when the data are EISM. Thus, the proposed HUSDOS-Boost is a useful methodology of HR data analysis.

Scopus

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/smc42975.2020.9283356

Web of Science

Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

OBJECTIVE: Driver drowsiness detection is a key technology that can prevent fatal car accidents caused by drowsy driving. The present work proposes a driver drowsiness detection algorithm based on heart rate variability (HRV) analysis and validates the proposed method by comparing with electroencephalography (EEG)-based sleep scoring. METHODS: Changes in sleep condition affect the autonomic nervous system and then HRV, which is defined as an RR interval (RRI) fluctuation on an electrocardiogram trace. Eight HRV features are monitored for detecting changes in HRV by using multivariate statistical process control, which is a well known anomaly detection method. RESULT: The performance of the proposed algorithm was evaluated through an experiment using a driving simulator. In this experiment, RRI data were measured from 34 participants during driving, and their sleep onsets were determined based on the EEG data by a sleep specialist. The validation result of the experimental data with the EEG data showed that drowsiness was detected in 12 out of 13 pre-N1 episodes prior to the sleep onsets, and the false positive rate was 1.7 times per hour. CONCLUSION: The present work also demonstrates the usefulness of the framework of HRV-based anomaly detection that was originally proposed for epileptic seizure prediction. SIGNIFICANCE: The proposed method can contribute to preventing accidents caused by drowsy driving.

DOI： 10.1109/TBME.2018.2879346

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PubMed

Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

© 2001-2011 IEEE. A focal brain cooling system for treatment of refractory epilepsy that is implantable and wearable may permit patients with this condition to lead normal daily lives. We have developed such a system for cooling of the epileptic focus by delivery of cold saline to a cooling device that is implanted cranially. The outflow is pumped for circulation and cooled by a Peltier device. Here, we describe the design of the system and evaluate its feasibility by simulation. Mathematical models were constructed based on equations of fluid dynamics and data from a cat model. Computational fluid dynamics simulations gave the following results: 1) a cooling device with a complex channel structure gives a more uniform temperature in the brain; 2) a cooling period of <10 min is required to reach an average temperature of 25.0°Cat 2 mm below the brain surface, which is the target temperature for seizure suppression. This time is short enough for cooling of the brain before seizure onset after seizure prediction by an intracranial electroencephalogram-based algorithm; and 3) battery charging would be required once every several days for most patients. These results suggest that the focal brain cooling system may be clinically applicable.

DOI： 10.1109/TNSRE.2019.2891090

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Scopus

PubMed

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

© 2001-2011 IEEE. Although early reperfusion therapy is effective for acute ischemic stroke, limited therapeutic time-window resulted in only 10% of patients receiving reperfusion therapy. A fast and reliable stroke detection method is desired so that patients can receive early reperfusion therapy. It has been reported that ischemic stroke affects heart rate variability (HRV), which reflects activities of the autonomic nervous function. Thus, ischemic stroke may be detected at an acute stage through monitoring HRV. This paper proposes an HRV-based ischemic stroke detection algorithm by using multivariate statistical process control (MSPC), which is a well-known anomaly detection algorithm. As a feasibility study before collecting a large amount of clinical data from human patients, this paper used the middle cerebral artery occlusion (MCAO) model in rats for collecting HRV data shortly after ischemic stroke onsets. The 11 MCAO-operated rats and 11 sham-operated rats were prepared, and HRV data of three sham-operated rats were used for model construction. The data on the other 19 rats were used for its validation. The experimental result showed that sensitivity and specificity of the proposed algorithm were 82% and 75%, respectively. Thus, the present work shows the possibility of realizing an HRV-based ischemic stroke detection system for human patients.

DOI： 10.1109/TNSRE.2018.2834554

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Scopus

PubMed

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.3389/fchem.2018.00171

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PubMed

Language：English   Publishing type：Research paper (scientific journal)  

Web of Science

Language：English   Publishing type：Research paper (scientific journal)  

Web of Science

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.sleep.2017.11.692

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.sleep.2017.11.293

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Language：English   Publishing type：Research paper (scientific journal)  

Web of Science

Language：English   Publishing type：Research paper (scientific journal)  

Web of Science

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/TBME.2015.2512276

PubMed

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.chemolab.2012.08.007

J-GLOBAL

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1002/aic.11791

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.trf.2024.01.014

Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.bspc.2023.105520

Web of Science

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/JBHI.2023.3349353

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

DOI： 10.1007/s12630-023-02653-6

PubMed

Other Link： https://link.springer.com/article/10.1007/s12630-023-02653-6/fulltext.html

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/apsipaasc58517.2023.10317498

Language：English   Publishing type：Research paper (scientific journal)   Publisher：MDPI AG  

Single nucleotide variants (SNVs) affecting the first nucleotide G of an exon (Fex-SNVs) identified in various diseases are mostly recognized as missense or nonsense variants. Their effect on pre-mRNA splicing has been seldom analyzed, and no curated database is available. We previously reported that Fex-SNVs affect splicing when the length of the polypyrimidine tract is short or degenerate. However, we cannot readily predict the splicing effects of Fex-SNVs. We here scrutinized the available literature and identified 106 splicing-affecting Fex-SNVs based on experimental evidence. We similarly identified 106 neutral Fex-SNVs in the dbSNP database with a global minor allele frequency (MAF) of more than 0.01 and less than 0.50. We extracted 115 features representing the strength of splicing cis-elements and developed machine-learning models with support vector machine, random forest, and gradient boosting to discriminate splicing-affecting and neutral Fex-SNVs. Gradient boosting-based LightGBM outperformed the other two models, and the length and nucleotide compositions of the polypyrimidine tract played critical roles in the discrimination. Recursive feature elimination showed that the LightGBM model using 15 features achieved the best performance with an accuracy of 0.80 ± 0.12 (mean and SD), a Matthews Correlation Coefficient (MCC) of 0.57 ± 0.15, an area under the curve of the receiver operating characteristics curve (AUROC) of 0.86 ± 0.08, and an area under the curve of the precision–recall curve (AUPRC) of 0.87 ± 0.09 using a 10-fold cross-validation. We developed a web service program, named FexSplice that accepts a genomic coordinate either on GRCh37/hg19 or GRCh38/hg38 and returns a predicted probability of aberrant splicing of A, C, and T variants.

DOI： 10.3390/genes14091765

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PubMed

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：日本睡眠学会・日本時間生物学会  

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：日本睡眠学会・日本時間生物学会  

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

Abstract

The i-gel, a popular second-generation supraglottic airway device, has been used in a variety of airway management situations, including as an alternative to tracheal intubation for general anesthesia, rescue in difficult airway settings, and out-of-hospital cardiac arrest resuscitation. We aimed to investigate the number of experiences needed to achieve a rapid, highly successful first attempt i-gel insertion in novices with a cumulative sum analysis. We also looked at how learning affected success rates, insertion time, and bleeding and reflex (limb movement, frowning face, or coughing) incidences. This prospective observational study included 15 novice residents from March 2017 to February 2018 in a tertiary teaching hospital. Finally, 13 residents with 35 [30–42] (median [interquartile range]) cases of i-gel insertion were analyzed. The cumulative sum analysis showed that 11 of 13 participants had an acceptable failure rate after 15 [8–20] cases. With increasing experience, success rate (P = 0.004), insertion time (P &lt; 0.001), and incidence of bleeding (P = 0.006) all improved. However, the incidence of reflex did not change (P = 0.43). Based on our results, we suggest that 20 cases are preferable for novices to develop skills in using the i-gel in airway management.

DOI： 10.1038/s41598-023-34152-5

PubMed

Other Link： https://www.nature.com/articles/s41598-023-34152-5

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

Abstract

Epilepsy is a neurological disorder that may affect the autonomic nervous system (ANS) from 15 to 20 min before seizure onset, and disturbances of ANS affect R–R intervals (RRI) on an electrocardiogram (ECG). This study aims to develop a machine learning algorithm for predicting focal epileptic seizures by monitoring R–R interval (RRI) data in real time. The developed algorithm adopts a self-attentive autoencoder (SA-AE), which is a neural network for time-series data. The results of applying the developed seizure prediction algorithm to clinical data demonstrated that it functioned well in most patients; however, false positives (FPs) occurred in specific participants. In a future work, we will investigate the causes of FPs and optimize the developing seizure prediction algorithm to further improve performance using newly added clinical data.

DOI： 10.1007/s10015-022-00832-0

Other Link： https://link.springer.com/article/10.1007/s10015-022-00832-0/fulltext.html

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Frontiers Media SA  

Coal has been an important energy source worldwide; however, it is the largest source of nitrogen oxide (NOx) emissions because the amount of nitrogen in coal is larger than that of other fossil fuels. Precise control of NOx emissions is required in operations of coal-fired power plants from the viewpoint of air pollution control. Although theoretical analyses of NOx generation from a coal-fired power plant have been conducted, it is difficult to precisely predict NOx generation in an actual plant. NOx generation is affected by various factors, such as furnace design and operating conditions, and there are complicated relationships among them. Thus, it is necessary to identify important operating factors that affect NOx generation in actual coal-fired power plants. A linear non-Gaussian acyclic model (LiNGAM) is an exploratory causal analysis method that identifies a causal ordering of variables and their connection strengths without any prior knowledge of causal relationships among variables. In this study, we analyzed real operation data collected from a coal-fired power plant using LiNGAM to identify factors of NOx generation. The causal relationship between process variables and NOx generation was estimated by means of LiNGAM, and the connectional strengths of the variables on NOx generation were derived. The analysis results agreed with previous reports on NOx generation mechanisms, such as combustion air temperature, steam temperature on a specific side of the furnace, and air flow rate of forced draft fans. In addition, we found the steam flow rate and the furnace pressure as new candidate factors of NOx generation through causal analysis using LiNGAM, which heretofore has not been suggested. Our analysis result should contribute to reducing NOx emissions from coal-fired power plants in the future.

DOI： 10.3389/frans.2023.1045324

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Heart rate variability (HRV) analysis plays an essential role in healthcare. HRV features cannot be extracted accurately from the R-R interval (RRI) when RRI data contains artifacts. Previous research for modifying RRI data with artifacts considered premature atrial contraction (PAC) and premature ventricular contraction (PVC), which are the most common types of extrasystoles occurring every day in healthy persons. This research proposed three new RRI modification algorithms for PAC and PVC using nearest neighbor search (NNS) algorithms: k-nearest neighbors (KNN), clustering-KNN (CKNN), and approximate nearest neighbors (ANN). The present work demonstrated that the ANN-based RRI modification (ANN-RM) algorithm achieved lower root mean squared errors (RMSEs) than the CKNN-based RRI modification algorithm and the highest computational speed. The RMSEs of ANN-RM for PAC and PVC were 23.0 ms and 26.2 ms, respectively.

Web of Science

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Japanese Society for Artificial Intelligence  

Sleep apnea syndrome (SAS) is a disorder in which breathing events such as apnea or hypopnea during sleep causes sleepiness and fatigue. SAS is a risk factor for coronary artery disease ( angina and myocardial infarction), atrial fibrillation, stroke, etc. The prevalence of SAS is reported to be 2-7% in adults; however, more patients with less subjective symptoms is estimated to have SAS. SAS is generally diagnosed by polysomnography (PSG) in specialized sleep institutes. However, PSG is performed only in a limited number of centers. Therefore, a screening method for SAS is needed to be developed. We focused on heart rate variability related to respiratory events and developed a screening method for SAS using neural networks. We examined a large PSG data set (N = 938) and attempted to detect SAS using long-term and short-term memory for heart rate data. Severe SAS was detected with an area under the curve (AUC) of 0.92, a sensitivity of 0.80, and a specificity of 0.84. We aim to develop a convenient screening method using a wearable device for the early detection of SAS.

DOI： 10.11517/pjsai.jsai2023.0_1l4os18a04

CiNii Research

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Japanese Society for Artificial Intelligence  

There is a need to develop objective and real-time postoperative pain assessment methods in perioperative medicine. Few studies have evaluated the relationship between pain severity and temporal changes of physiological signals in actual postoperative patients. The aim of the study was to evaluate postoperative pain continuously and to predict pain exacerbation in real-time. We focused on intravenous patient-controlled analgesia (IV-PCA), a common analgesic modality utilized in post-surgical patients. We chose an electrocardiogram (ECG) as a feature to detect pain exacerbation. We developed a machine learning model which was trained from IV-PCA records and ECG of postoperative patients to predict pain exacerbation. A self-attentive autoencoder (SA-AE) model achieved 54% of sensitivity and a 1.76 times/h of false positive rate. In summary, we propose a novel pain evaluation method using an IV-PCA device. According to the current findings, ECG features may be used to predict postoperative pain exacerbation in real-time.

DOI： 10.11517/pjsai.jsai2023.0_1l5os18b02

CiNii Research

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.3390/pr10112269

Web of Science

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Frontiers Media SA  

It is desirable to improve sleep quality since poor sleep results in decreases in work productivity and increases in risks of lifestyle-related diseases. Sleep spindles in sleep EEG are waveforms that characterize non-REM sleep Stage 2 (Stage N2). Music therapy has been adopted as a non-pharmacological therapy for sleep quality improvement; however, few studies mention the relationship between music during sleep and spindles. We conducted a crossover randomized study to investigate music's effects on spindles and sleep parameters. Polysomnography (PSG) was performed on 12 adult males with sleep difficulties over three nights, during which they were exposed to three different acoustic environments–silent, white noise, and pleasant sounds–throughout the night, in a crossover randomized setting. Half of the participants with large WASO were defined as the sleep maintenance difficulty group. We investigated whether pleasant sounds shortened sleep onset latency (SOL) and increased the number of spindles (SN) and spindle density (SD) compared to white noise, using silent as the reference. The spindles were detected using the previously reported automatic spindle detection algorithm. After one patient was excluded due to data corruption, a total of 11 participants, including the sleep maintenance difficulty group (n = 5), were analyzed. For all participants, SOL was not significantly shorter with pleasant sound than with white noise (p = 0.683); for the sleep maintenance difficulty group, SOL tended to be shorter with pleasant sound than with white noise (p = 0.060). Compared to white noise, the SN increased in pleasant sound for 7 of 11 (4 of 5 in the sleep maintenance difficulty group), and SD increased for 5 of 11 (3 of 5 in the sleep maintenance difficulty group). The results suggest that all-night background sound exposure may affect SN and SD. Future research should investigate whether background sound exposure reduces sleep-related distress, achieves sound sleep, or improves daytime psychomotor function.

DOI： 10.3389/frsle.2022.986333

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

DOI： 10.1007/s10015-022-00815-1

Other Link： https://link.springer.com/article/10.1007/s10015-022-00815-1/fulltext.html

Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.3389/frsle.2022.975415

Language：English   Publishing type：Research paper (scientific journal)  

The evidence that heart rate variability (HRV) decreases during early Parkinson's disease (PD) largely depends on electrocardiogram data. In this study, we examined HRV in PD using wearable sensors and assessed various evaluation methods for detecting disease-related alterations. We evaluated 27 patients with PD and 23 disease controls. The wearable sensors POLAR V800 HR and POLAR H10 were used for the HRV measurements. The participants wore the two sensors for approximately 24 h, and long-term HRV data were acquired. We analyzed the standard deviation of normal R-R intervals (SDNN) and coefficient of variation of R-R intervals (CVRR) for every 100 consecutive beats. Focusing on the fluctuation of SDNN and CVRR, we extracted the minimum, first decile, first quartile, and median values of SDNN and CVRR. The area under the receiver operating characteristic curve (AUC) for each HRV parameter was calculated to differentiate PD from the disease controls. The minimum values of SDNN and CVRR had the highest AUC (SDNN: AUC 0.90, 95% confidence interval [CI] 0.78-0.96; CVRR: AUC 0.90, CI 0.76-0.96) among the evaluation methods tested. The minimum values of SDNN and CVRR were significantly decreased in PD (SDNN: 9.5 ± 4.0 ms vs. 4.4 ± 2.0 ms, p < 0.0001; CVRR: 1.15 ± 0.33% vs. 0.65 ± 0.24%, p < 0.0001). We detected decreased HRV in PD using wearable sensors. Analyzing the minimum values of the HRV parameter in long-term recordings appears to be appropriate for detecting the decrease in HRV in PD.

DOI： 10.1007/s00702-022-02528-y

Web of Science

PubMed

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：麻酔科学サマーセミナー事務局  

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

OBJECTIVE: The purpose of this study is to investigate changes in autonomic activities and systemic circulation generated by surgical manipulation or electrical stimulation to the human brain stem. METHODS: We constructed a system that simultaneously recorded microsurgical field videos and heart rate variability (HRV) that represent autonomic activities. In 20 brain stem surgeries recorded, HRV features and sites of surgical manipulation were analyzed in 19 hypertensive epochs, defined as the periods with transient increases in the blood pressure. We analyzed the period during electrical stimulation to the ponto-medullary junction, performed for the purpose of monitoring a cranial nerve function. RESULTS: In the hypertensive epoch, HRV analysis showed that sympathetic activity predominated over the parasympathetic activity. The hypertensive epoch was more associated with surgical manipulation of the area in the caudal pons or the rostral medulla oblongata compared to controls. During the period of electrical stimulation, there were significant increases in blood pressures and heart rates, accompanied by sympathetic overdrive. CONCLUSIONS: Our results provide physiological evidence that there is an important autonomic center located adjacent to the ponto-medullary junction. SIGNIFICANCE: A large study would reveal a candidate target of neuromodulation for disorders with autonomic imbalances such as drug-resistant hypertension.

DOI： 10.1016/j.clinph.2021.03.006

Web of Science

PubMed

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Institute of Electrical and Electronics Engineers (IEEE)  

DOI： 10.1109/tiv.2020.3029260

Web of Science

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：MDPI AG  

Heart rate variability, which is the fluctuation of the R-R interval (RRI) in electrocardiograms (ECG), has been widely adopted for autonomous evaluation. Since the HRV features that are extracted from RRI data easily fluctuate when arrhythmia occurs, RRI data with arrhythmia need to be modified appropriately before HRV analysis. In this study, we consider two types of extrasystoles—premature ventricular contraction (PVC) and premature atrial contraction (PAC)—which are types of extrasystoles that occur every day, even in healthy persons who have no cardiovascular diseases. A unified framework for ectopic RRI detection and a modification algorithm that utilizes an autoencoder (AE) type of neural network is proposed. The proposed framework consists of extrasystole occurrence detection from the RRI data and modification, whose targets are PVC and PAC. The RRI data are monitored by means of the AE in real time in the detection phase, and a denoising autoencoder (DAE) modifies the ectopic RRI caused by the detected extrasystole. These are referred to as AE-based extrasystole detection (AED) and DAE-based extrasystole modification (DAEM), respectively. The proposed framework was applied to real RRI data with PVC and PAC. The result showed that AED achieved a sensitivity of 93% and a false positive rate of 0.08 times per hour. The root mean squared error of the modified RRI decreased to 31% in PVC and 73% in PAC from the original RRI data by DAEM. In addition, the proposed framework was validated through application to a clinical epileptic seizure problem, which showed that it correctly suppressed the false positives caused by PVC. Thus, the proposed framework can contribute to realizing accurate HRV-based health monitoring and medical sensing systems.

DOI： 10.3390/s21093235

Web of Science

Scopus

PubMed

Language：English   Publishing type：Research paper (scientific journal)  

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

The present study investigates the factors of "Weekday sleep debt (WSD)" by comparing activity data collected from persons with and without WSD. Since it has been reported that the amount of sleep debt as well the difference between the social clock and the biological clock is associated with WSD, specifying the factors of WSD other than chronotype may contribute to sleep debt prevention. We recruited 324 healthy male employees working at the same company and collected their one-week wrist actigraphy data and answers to questionnaires. Because 106 participants were excluded due to measurement failure of the actigraphy data, the remaining 218 participants were included in the analysis. All participants were classified into WSD or non-WSD groups, in which persons had WDS if the difference between their weekend sleep duration and the mean weekday sleep duration was more than 120 min. We evaluated multiple measurements derived from the collected actigraphy data and trained a classifier that predicts the presence of WSD using these measurements. A support vector machine (SVM) was adopted as the classifier. In addition, to evaluate the contribution of each indicator to WSD, permutation feature importance was calculated based on the trained classifier. Our analysis results showed significant importance of the following three out of the tested 32 factors: 1) WSD was significantly related to persons with evening tendency. 2) Daily activity rhythms and sleep were less stable in the WSD group than in the non-WSD group. 3) A specific day of the week had the highest importance in our data, suggesting that work habit contributes to WSD. These findings indicate some WSD factors: evening chronotype, instability of the daily activity rhythm, and differences in work habits on the specific day of the week. Thus, it is necessary to evaluate the rhythms of diurnal activities as well as sleep conditions to identify the WSD factors. In particular, the diurnal activity rhythm influences WSD. It is suggested that proper management of activity rhythm may contribute to the prevention of sleep debt.

DOI： 10.3389/fpubh.2021.630640

Web of Science

PubMed

Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.3389/fpubh.2020.00178

Language：English   Publishing type：Research paper (scientific journal)   Publisher：MDPI AG  

A warning prior to seizure onset can help improve the quality of life for epilepsy patients. The feasibility of a wearable system for predicting epileptic seizures using anomaly detection based on machine learning is evaluated. An original telemeter is developed for continuous measurement of R-R intervals derived from an electrocardiogram. A bespoke smartphone app calculates the indices of heart rate variability in real time from the R-R intervals, and the indices are monitored using multivariate statistical process control by the smartphone app. The proposed system was evaluated on seven epilepsy patients. The accuracy and reliability of the R-R interval measurement, which was examined in comparison with the reference electrocardiogram, showed sufficient performance for heart rate variability analysis. The results obtained using the proposed system were compared with those obtained using the existing video and electroencephalogram assessments; it was noted that the proposed method has a sensitivity of 85.7% in detecting heart rate variability change prior to seizures. The false positive rate of 0.62 times/h was not significantly different from the healthy controls. The prediction performance and practical advantages of portability and real-time operation are demonstrated in this study.

DOI： 10.3390/s20143987

Web of Science

PubMed

Language：English   Publishing type：Research paper (scientific journal)  

© 2020 Elsevier Ltd Evaluating the importance of input (predictor) variables is of interest in many applications of statistical models. However, nonlinearity and correlation among variables make it difficult to measure variable importance accurately. In this work, a novel variable importance measure, called regression and independence based variable importance (RIVI), is proposed. RIVI is designed by integrating Gaussian process regression (GPR) and Hilbert-Schmidt independence criterion (HSIC) so that it is applicable to nonlinear systems. The results of two numerical examples demonstrate that RIVI is superior to several conventional measures including the Pearson correlation coefficient, PLS-β, PLS-VIP, Lasso, HSIC, and permutation importance with random forest in the variable identification accuracy.

DOI： 10.1016/j.compchemeng.2020.106757

Web of Science

Scopus

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

Sleep spindles are important electroencephalographic (EEG) waveforms in sleep medicine; however, it is burdensome even for experts to detect spindles, so automatic spindle detection methodologies have been investigated. Conventional methods utilize waveforms template matching or machine learning for detecting spindles. In the former approach, it is necessary to tune thresholds for individual adaptation, while the latter approach has the problem of imbalanced data because the amount of sleep spindles is small compared with the entire EEG data. The present work proposes a sleep spindle detection method that combines wavelet synchrosqueezed transform (SST) and random under-sampling boosting (RUSBoost). SST is a time-frequency analysis method suitable for extracting features of spindle waveforms. RUSBoost is a framework for coping with the imbalanced data problem. The proposed SST-RUS can deal with the imbalanced data in spindle detection and does not require threshold tuning because RUSBoost uses majority voting of weak classifiers for discrimination. The performance of SST-RUS was validated using an open-access database called the Montreal archives of sleep studies cohort 1 (MASS-C1), which showed an F-measure of 0.70 with a sensitivity of 76.9% and a positive predictive value of 61.2%. The proposed method can reduce the burden of PSG scoring.

DOI： 10.1109/TNSRE.2020.2964597

Web of Science

PubMed

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society of Animal Psychology  

<p>Heart rate variability (HRV) is a physical and noninvasive index of the autonomic nervous system and has been used in a wide range of fields such as human medicine, veterinary and animal behavior. Measuring devices have been improved miniaturization and light-weighting and they make it possible to measure a dog's electrocardiogram (ECG) under a free moving condition. HRV has been known as an index not only of physical activity but also to evaluate an animal's emotional status. One concern is a difficulty in dissociating physical activity and emotional status in HRV parameters. In this study, we examined how the physical activity component and the emotional component affect in HRV. We measured HRV and acceleration of the dogs under two conditions, the physical activity (motion) condition and the reward condition where food treats were emotional stimuli and under the motion condition. As a result, a dog's HRV values were linearly regressed on the acceleration data. SDNN (Standard Deviation of NN intervals) affected by the composite acceleration in reward condition while rMSSD (root Mean Square of Successive Differences) affected in motion condition. These suggested that the physical activity and HRV indices distribute on regression lines and emotional stimuli influences each HRV indices differently.</p>

DOI： 10.2502/janip.70.1.1

CiNii Research

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

OBJECTIVE: Obstructive sleep apnea (OSA) is a common sleep disorder; however, most patients are undiagnosed and untreated because it is difficult for patients themselves to notice OSA in daily living. Polysomnography (PSG), which is the gold standard test for sleep disorder diagnosis, cannot be performed in many hospitals. This fact motivates us to develop a simple system for screening OSA at home. APPROACH: The autonomic nervous system changes during apnea, and such changes affect heart rate variability (HRV). This work develops a new apnea screening method based on HRV analysis and machine learning technologies. An apnea/normal respiration (A/N) discriminant model is built for respiration condition estimation for every heart rate measurement, and an apnea/sleep ratio is introduced for final diagnosis. A random forest is adopted for the A/N discriminant model construction, which is trained with the PhysioNet apnea-ECG database. MAIN RESULTS: The screening performance of the proposed method was evaluated by applying it to clinical PSG data. Sensitivity and specificity achieved 76% and 92%, respectively, which are comparable to existing portable sleep monitoring devices used in sleep laboratories. SIGNIFICANCE: Since the proposed OSA screening method can be used more easily than existing devices, it will contribute to OSA treatment.

DOI： 10.1088/1361-6579/ab57be

Web of Science

PubMed

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier {BV}  

DOI： 10.1016/j.sleep.2019.11.727

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：(一社)日本医療情報学会  

Language：English   Publishing type：Research paper (scientific journal)  

DOI： https://doi.org/10.3389/fpsyg.2019.01678

Language：English   Publishing type：Research paper (scientific journal)  

Sleep apnea syndrome (SAS) is a prevalent disorder which causes daytime fatigue with the increased risk of lifestyle diseases. A large number of patients are undiagnosed and untreated partly because of the difficulty in performing its gold standard test, polysomnography (PSG). In this research, we propose a simple screening method utilizing heart rate variability (HRV) and long short-term memory (LSTM) which is a kind of neural network techniques. The result of applying this algorithm to clinical data demonstrates that it can discriminate between patients and healthy people with high sensitivity (100%) and specificity (100%).

DOI： 10.1109/EMBC.2019.8856463

Web of Science

PubMed

Language：English   Publishing type：Research paper (scientific journal)  

Emotional contagion is a primitive form of empathy that does not need higher psychological functions. Recent studies reported that emotional contagion exists not only between humans but also among various animal species. The dog (Canis familiaris) is a unique animal and the oldest domesticated species. Dogs have coexisted with humans for more than 30,000 years and are woven into human society as partners bonding with humans. Dogs have acquired human-like communication skills and, likely as a result of the domestication process, the ability to read human emotions; therefore, it is feasible that there may be emotional contagion between human and dogs. However, the higher time-resolution of measurement of emotional contagion between them is yet to be conducted. We assessed the emotional reactions of dogs and humans by heart rate variability (HRV), which reflects emotion, under a psychological stress condition on the owners. The correlation coefficients of heart beat (R-R) intervals (RRI), the standard deviations of all RR intervals (SDNN), and the square root of the mean of the sum of the square of differences between adjacent RR intervals (RMSSD) between dogs and owners were positively correlated with the duration of dog ownership. Dogs' sex also influenced the correlation coefficients of the RRI, SDNN, and RMSSD in the control condition; female showed stronger values. These results suggest that emotional contagion from owner to dog can occur especially in females and the time sharing the same environment is the key factor in inducing the efficacy of emotional contagion.

DOI： 10.3389/fpsyg.2019.01678

Web of Science

PubMed

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. The R-R interval (RRI) fluctuation in electrocardiogram (ECG) is called heart rate variability (HRV), which reflects activities of the autonomic nervous system (ANS) and has been used for various health monitoring services. Accurate R wave detection is crucial for success in HRV-based health monitoring services; however, ECG artifacts often cause missing R waves and deteriorate the accuracy of HRV analysis. The present work proposes a new missing RRI interpolation technique based on Just-In-Time (JIT) modeling. In the JIT modeling framework, a local regression model is built by weighing samples stored in the database according to the distance from a query and output is estimated only when an estimate is requested. The proposed method builds a local model and estimates missing RRI only when an RRI detection error is detected. Locally weighted partial least squares (LWPLS) is adopted for local model construction. The proposed method is referred to as LWPLS-based RRI interpolation (LWPLS-RI). The performance of the proposed LWPLS-RI was evaluated through its application to RRI data with artificial missing RRIs. We used the MIT-BIH Normal Sinus Rhythm Database for nominal RRI dataset construction. Missing RRIs were artificially introduced and they were interpolated by the proposed LWPLS-RI. In addition, MEAN that replaces the missing RRI by a mean of the past RRI data was compared as a conventional method. The result showed that the proposed LWPLS-RI improved root mean squared error (RMSE) of RRI by about 70% in comparison with MEAN. In addition, the proposed method realized precise HRV analysis. The proposed method will contribute to the realization of precise HRV-based health monitoring services.

DOI： 10.3390/s18113870

Web of Science

Scopus

PubMed

Language：English   Publishing type：Research paper (international conference proceedings)  

© 2018 IEEE. The fluctuation of an RR interval (RRI) on an electrocardiogram (ECG) is called heart rate variability (HRV). HRV reflects the autonomic nerve activity, thus HRV analysis has been used for health monitoring such as stress estimation, drowsiness detection, epileptic seizure prediction, and cardiovascular disease diagnosis. However, RRI and HRV features are easily affected by arrhythmia, which deteriorates the health monitoring performance. Premature ventricular contraction (PVC) is common arrhythmia that many healthy persons have. Thus, a new methodology for dealing with RRI fluctuation disturbed by PVC needs to be developed for realizing precise health monitoring. To modify RRI data affected by PVC, the present work proposes a new method based on a denoising autoencoder (DAE), which reconstructs original input data from the noisy input data by using a neural network. The proposed method, referred to as DAE-based RRI modification (DAERM), aims to correct the disturbed RRI data by regarding PVC as artifacts. The present work demonstrated the usefulness of the proposed DAE-RM through its application to real RRI data with artificial PVC (PVC-RRI). The result showed that DAE-RM successfully modified PVC-RRI data. In fact, the root means squared error (RMSE) of the modified RRI was improved by 83.5% from the PVC-RRI. The proposed DAERM will contribute to realizing precise HRV-based health monitoring in the future.

DOI： 10.1109/EMBC.2018.8513218

Scopus

PubMed

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

OBJECTIVE: Hemifacial spasm (HFS) is caused by arterial conflict at the root exit zone of the facial nerve. As the offending artery is pulsatile in nature, this study investigated the association of heart rate fluctuation with HFS. METHODS: Twenty-four preoperative patients underwent simultaneous recordings of facial electromyogram and electrocardiogram overnight. Series of R-wave to R-wave intervals (RRIs) in the electrocardiogram were analyzed across subjects in relation to HFS. The degree of heart rate fluctuation was quantified by analyzing the heart rate variability (HRV). The sleep stage was evaluated during the period of HFS. RESULTS: A 0.1 Hz fluctuation in RRIs by 5% compared to the baseline preceded a few seconds the onset of the HFS, indicating that a significant increase in the heart rate coincided with HFS. HRV analysis demonstrated that fluctuations in the heart rate were significantly enhanced during HFS. Wake or light sleep stages were more often accompanied by HFS, suggesting an association with autonomic activities. CONCLUSION: Our findings suggest that the etiology of HFS is more than just a mechanical compression of the facial nerve and may involve changes in pulsatile frequency in offending arteries. SIGNIFICANCE: We propose the etiology of HFS from a unique standpoint.

DOI： 10.1016/j.clinph.2018.07.003

Web of Science

PubMed

Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (international conference proceedings)  

© 2018 IEEE. When players are excited by playing a video game, corresponding physiological responses such as sweating or changes in heart rate may appear. It is assumed that presenting physiological responses during game play to players in real-time alters their game experience even when they play the same game. Based on this assumption, this work investigated the effect of false heart rate (HR) feedback on game experience through experiments using a simple action game. Our experimental results indicated that false HR feedback not only prevented the players from becoming tired of the game but also enhanced players' experiences. In addition, a new game controller that can present HR information audibly and tactually was developed for realizing a game system based on false HR feedback.

DOI： 10.1109/ICCE.2018.8326254

Scopus

Language：English   Publishing type：Part of collection (book)  

© 2018 Elsevier B.V. Epilepsy is a group of neurological disorders which is caused by excessive neuronal activities in cerebrum and characterized by recurrent seizures. A quarter of patients have intractable epilepsy and do not become seizure-free with medication. We are developing an implantable and wearable focal brain cooling system, which enables the patients to lead ordinary daily life. The system cools the epileptic focus, where the excessive neuronal activities begin, by delivering cold saline to a cranially implanted cooling device. In this research, we developed a whole system model through the first principles and animal experiments. The results of system design have shown that a cooling device with more complex channel structure achieves higher temperature uniformity in the brain with lower flow rate of saline. The optimal structure was derived by taking account of the trade-off between pressure drop and temperature uniformity. In addition, the results have demonstrated that the cooling duration is less than 10 minutes for the average temperature 2 mm below the cooling device (inside the brain) to reach 25 °C; it is short enough to cool the brain after seizure is predicted by existing electroencephalogram (EEG)-based algorithms. Moreover, the frequency of battery charging would be once in several days for most patients.

DOI： 10.1016/B978-0-444-64241-7.50343-8

Scopus

Language：English   Publisher：Elsevier BV  

DOI： 10.1016/j.sleep.2017.11.1038

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.jns.2017.08.3769

Language：English   Publishing type：Research paper (international conference proceedings)  

© 2017 IEEE. Objective: The present study proposes a cerebral infarction detection algorithm based on heart rate variability (HRV). Methods: It has been reported that infarction affects HRV. Therefore, infarction could be detected at an acute stage by monitoring HRV. This study uses multivariate statistical process control (MSPC), which is a well-known anomaly monitoring method. HRV data shortly after infarction onsets are collected by using the middle cerebral artery occlusion (MCAO) model in rats. This study prepares 11 MCAO-operated rats and 11 sham-operated rats. Three sham-operated rats' data are used for model construction of MSPC, and the other 19 rats' data are used for its validation. Results: The sensitivity and specificity of the proposed algorithm were 82 % and 75 %, respectively. Conclusion: An infarction onset could be detected at an acute stage by monitoring HRV.

DOI： 10.1109/EMBC.2017.8037503

Web of Science

Scopus

PubMed

Other Link： http://orcid.org/0000-0002-2325-1043

Language：English   Publishing type：Research paper (international conference proceedings)  

© 2017 IEEE. Epilepsy is a group of diseases caused by excessive neuronal activities, and one-quarter of the patients do not become seizure-free by the existing treatments. The potential treatments include focal brain cooling, which aims to cool the region where the excessive neuronal activities begin. We are developing a focal brain cooling system. The system delivers cold saline to a cranially implanted cooling device. The outflow is cooled by a Peltier device and pumped for circulation. The Peltier device and the pump are activated only when a seizure is predicted. In this research, the length of time for cooling the brain was calculated with a computational fluid dynamics (CFD)-based model of the focal brain cooling system. As a result, it takes less than 10 minutes for the average temperature 2 mm below the cooling device to reach 25.0 °C. It is much shorter than the time from seizure prediction to seizure onset when an existing algorithm for prediction is used.

DOI： 10.1109/EMBC.2017.8036817

Web of Science

Scopus

PubMed

Language：English   Publishing type：Research paper (international conference proceedings)  

© 2017 IEEE. Although process monitoring is important for maintaining safety and product quality, it is difficult to understand process characteristics particularly when they are changing. Since the correlation among variables changes due to changes in process characteristics, process data visualization based on the correlation among variables helps process characteristic understanding. In the present work, a new correlation-based data visualization method is proposed by integrating joint decorrelation (JD) and stochastic proximity embedding (SPE). JD is a blind source separation (BSS) method that can separates sample based on the correlation, and SPE is a self-organizing algorithm that can map high-dimensional data to a two-dimensional plane. The proposed method, referred to as JD-SPE, separates samples based on the correlation using JD and the separated samples are visualized in the two-dimensional plane by SPE. Correlation matrices have to be constructed before sample separation for JD; however how to construct them is not clear. The present work also proposes a correlation matrix construction method for JD by using nearest correlation spectral clustering (NCSC), which is a correlation-based clustering method. In addition, a new process monitoring method based on multivariate statistical process control (MSPC) which is a well-known process monitoring algorithm and JD-SPE. This monitoring method is referred to as JD-SPE-r2. The proposed JD-SPE-Γ2 can detect a fault that can not detected by the conventional MSPC. The usefulness of the proposed methods is demonstrated through numerical examples.

DOI： 10.1109/ICCA.2017.8003187

Scopus

Language：Japanese   Publishing type：Research paper (scientific journal)  

DOI： 10.11351/jsaeronbun.48.463

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.3389/fneur.2016.00110

PubMed

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.9746/jcmsi.9.10

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.chemolab.2015.09.009

Language：English   Publishing type：Research paper (scientific journal)  

Appropriate input variables have to be selected for building highly accurate soft sensor. A novel input variable selection method based on nearest correlation spectral clustering (NCSC) has been proposed, and it is referred to as NCSC-based variable selection (NCSC-VS). Although NCSC-VS can select appropriate input variables, a lot of parameters have to be tuned carefully for selecting proper variables. The present work proposes a new methodology for efficient input variable selection by integrating NCSC and group Lasso. The proposed NCSC-based group Lasso (NCSC-GL) can not only reduce the number of tuning parameters but also achieve almost the same performance as NCSC-VS. The usefulness of the proposed NCSC-GL is demonstrated through applications to soft sensor design for a pharmaceutical process and a chemical process.

DOI： 10.1016/j.isatra.2015.04.007

Scopus

PubMed

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.9746/jcmsi.8.74

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1252/jcej.12we167

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.conengprac.2010.11.013

J-GLOBAL

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.jprocont.2011.06.023

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.chemolab.2010.02.006

J-GLOBAL

Language：Japanese   Publishing type：Research paper (scientific journal)  

DOI： 10.9746/ve.sicetr1965.44.317

Language：Japanese   Publishing type：Research paper (scientific journal)  

DOI： 10.9746/sicetr1965.42.1143

J-GLOBAL

Language：Japanese   Publishing type：Research paper (scientific journal)  

DOI： 10.9746/sicetr1965.42.909

Language：Japanese   Publishing type：Research paper (scientific journal)  

DOI： 10.9746/sicetr1965.42.902

J-GLOBAL

Language：Japanese  

J-GLOBAL

Language：Japanese   Publisher：(一社)日本臨床神経生理学会  

Language：English  

DOI： 10.1109/TNSRE.2020.2964597

PubMed

Language：English  

DOI： 10.3389/fpubh.2020.00178

PubMed

J-GLOBAL

J-GLOBAL

Language：English  

DOI： 10.1088/1361-6579/ab57be

PubMed

Language：Japanese   Publisher：(一社)日本医療情報学会  

Language：English  

Sleep apnea syndrome (SAS) is a prevalent disorder which causes daytime fatigue with the increased risk of lifestyle diseases. A large number of patients are undiagnosed and untreated partly because of the difficulty in performing its gold standard test, polysomnography (PSG). In this research, we propose a simple screening method utilizing heart rate variability (HRV) and long short-term memory (LSTM) which is a kind of neural network techniques. The result of applying this algorithm to clinical data demonstrates that it can discriminate between patients and healthy people with high sensitivity (100%) and specificity (100%).

DOI： 10.1109/EMBC.2019.8856463

Web of Science

PubMed

Language：Japanese   Publisher：(一社)日本睡眠学会  

Language：Japanese   Publisher：(一社)日本睡眠学会  

Language：Japanese   Publisher：(一社)日本睡眠学会  

Language：Japanese  

Language：English  

DOI： 10.1109/TNSRE.2019.2891090

PubMed

Language：English  

DOI： 10.3389/fpsyg.2019.01678

PubMed

Language：Japanese   Publisher：The Japanese Society for Artificial Intelligence  

<p>Sleep apnea syndrome (SAS) is a prevalent disorder which causes daytime fatigue with increased risk of cardiovascular diseases. A large number of patients are undiagnosed and untreated partly because of the difficulty in performing its gold standard test, polysomnography (PSG). In this research, we propose a simple screening method utilizing heart rate variability (HRV) and long short-term memory (LSTM) which is a kind of the neural network techniques. The result of applying this algorithm to clinical data demonstrates that it can discriminate between patients and healthy people with sensitivity (100%) and specificity (100%).</p>

DOI： 10.11517/pjsai.JSAI2019.0_1H4J1303

CiNii Research

J-GLOBAL

J-GLOBAL

J-GLOBAL

Language：English   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

Language：English   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

Language：English  

DOI： 10.1109/EMBC.2018.8513218

PubMed

Language：Japanese   Publisher：(一社)日本睡眠学会  

Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

J-GLOBAL

Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

J-GLOBAL

Language：English   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

© 2018 IEEE. When players are excited by playing a video game, corresponding physiological responses such as sweating or changes in heart rate may appear. It is assumed that presenting physiological responses during game play to players in real-time alters their game experience even when they play the same game. Based on this assumption, this work investigated the effect of false heart rate (HR) feedback on game experience through experiments using a simple action game. Our experimental results indicated that false HR feedback not only prevented the players from becoming tired of the game but also enhanced players' experiences. In addition, a new game controller that can present HR information audibly and tactually was developed for realizing a game system based on false HR feedback.

DOI： 10.1109/ICCE.2018.8326254

Scopus

Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

J-GLOBAL

Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

J-GLOBAL

Language：English   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

© 2017 IEEE. In the manufacturing industry, it is extremely important to identify variables that affect product quality. Identifying variables which affect quality variables is called causal analysis. In batch processes, time-series data of process variables and the corresponding data of quality variables are generally acquired. Since causal analysis using the raw data needs a large computation load, it is often performed after compressing time-series process variables data into non-time-series feature variables data. Various causal analysis methods using such data have been developed, however, none have shown effective results in actual plants. In the present work, non-time-series kernel Granger causality (NTS-KGC) is proposed for causal analysis with non-time-series data of batch processes. This is a method that kernel Granger causality [1], which is used for causal analysis with time-series data in nonlinear systems, is expanded for causal analysis with non-time-series data. The validity of the proposed method is demonstrated through a numerical example of a nonlinear batch process. In addition, we conducted a case study of applying NTS-KGC to data obtained from a real steelmaking process. The results demonstrate that NTS-KGC is superior to other existing methods using the following indexes, i.e. variable influence on projection (VIP) of partial least squares (PLS), regression coefficients of PLS, and variable importance of Random Forest.

DOI： 10.1109/ASCC.2017.8287443

Scopus

Other Link： http://orcid.org/0000-0002-2325-1043

Language：English   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

© 2018 Elsevier B.V. Epilepsy is a group of neurological disorders which is caused by excessive neuronal activities in cerebrum and characterized by recurrent seizures. A quarter of patients have intractable epilepsy and do not become seizure-free with medication. We are developing an implantable and wearable focal brain cooling system, which enables the patients to lead ordinary daily life. The system cools the epileptic focus, where the excessive neuronal activities begin, by delivering cold saline to a cranially implanted cooling device. In this research, we developed a whole system model through the first principles and animal experiments. The results of system design have shown that a cooling device with more complex channel structure achieves higher temperature uniformity in the brain with lower flow rate of saline. The optimal structure was derived by taking account of the trade-off between pressure drop and temperature uniformity. In addition, the results have demonstrated that the cooling duration is less than 10 minutes for the average temperature 2 mm below the cooling device (inside the brain) to reach 25 °C; it is short enough to cool the brain after seizure is predicted by existing electroencephalogram (EEG)-based algorithms. Moreover, the frequency of battery charging would be once in several days for most patients.

DOI： 10.1016/B978-0-444-64241-7.50343-8

Scopus

Language：Japanese   Publisher：(一社)日本てんかん学会  

J-GLOBAL

Language：English  

DOI： 10.3389/fchem.2018.00171

PubMed

Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)   Publisher：Japanese Society for Medical and Biological Engineering  

&lt;p&gt;The present study aims to predict generalized seizures based on heart rate variability (HRV) analysis and multivariate statistical process control (MSPC). We applied the existing anomaly monitoring algorithm, i.e. HRV-based MSPC developed by Fujiwara et al. to 17 pre-ictal episodes and 74 inter-ictal episodes whose total length were about 63 hours. Consequently, the sensitivity and the false positive rate were 76.5% and 1.39 times per hour respectively, and the proportion of duration under false alarms was 5.96%. These results suggest that generalized seizures may be predicted by analyzing HRV. Based on the previous research, we hypothesize that the change in the autonomic nervous activity induce generalized epileptic seizures.&lt;/p&gt;

J-GLOBAL

Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

J-GLOBAL

Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

J-GLOBAL

Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

J-GLOBAL

Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

J-GLOBAL

Language：Japanese   Publisher：メディカルレビュー社  

CiNii Books

Other Link： http://search.jamas.or.jp/link/ui/2017406986

Language：English   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

J-GLOBAL

Language：English   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

© 2017 IEEE. Objective: The present study proposes a cerebral infarction detection algorithm based on heart rate variability (HRV). Methods: It has been reported that infarction affects HRV. Therefore, infarction could be detected at an acute stage by monitoring HRV. This study uses multivariate statistical process control (MSPC), which is a well-known anomaly monitoring method. HRV data shortly after infarction onsets are collected by using the middle cerebral artery occlusion (MCAO) model in rats. This study prepares 11 MCAO-operated rats and 11 sham-operated rats. Three sham-operated rats' data are used for model construction of MSPC, and the other 19 rats' data are used for its validation. Results: The sensitivity and specificity of the proposed algorithm were 82 % and 75 %, respectively. Conclusion: An infarction onset could be detected at an acute stage by monitoring HRV.

DOI： 10.1109/EMBC.2017.8037503

Scopus

PubMed

Language：English   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

© 2017 IEEE. Epilepsy is a group of diseases caused by excessive neuronal activities, and one-quarter of the patients do not become seizure-free by the existing treatments. The potential treatments include focal brain cooling, which aims to cool the region where the excessive neuronal activities begin. We are developing a focal brain cooling system. The system delivers cold saline to a cranially implanted cooling device. The outflow is cooled by a Peltier device and pumped for circulation. The Peltier device and the pump are activated only when a seizure is predicted. In this research, the length of time for cooling the brain was calculated with a computational fluid dynamics (CFD)-based model of the focal brain cooling system. As a result, it takes less than 10 minutes for the average temperature 2 mm below the cooling device to reach 25.0 °C. It is much shorter than the time from seizure prediction to seizure onset when an existing algorithm for prediction is used.

DOI： 10.1109/EMBC.2017.8036817

Scopus

PubMed

Language：Japanese  

DOI： 10.11509/isciesci.61.9_381

Language：English   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

© 2017 IEEE. Although process monitoring is important for maintaining safety and product quality, it is difficult to understand process characteristics particularly when they are changing. Since the correlation among variables changes due to changes in process characteristics, process data visualization based on the correlation among variables helps process characteristic understanding. In the present work, a new correlation-based data visualization method is proposed by integrating joint decorrelation (JD) and stochastic proximity embedding (SPE). JD is a blind source separation (BSS) method that can separates sample based on the correlation, and SPE is a self-organizing algorithm that can map high-dimensional data to a two-dimensional plane. The proposed method, referred to as JD-SPE, separates samples based on the correlation using JD and the separated samples are visualized in the two-dimensional plane by SPE. Correlation matrices have to be constructed before sample separation for JD; however how to construct them is not clear. The present work also proposes a correlation matrix construction method for JD by using nearest correlation spectral clustering (NCSC), which is a correlation-based clustering method. In addition, a new process monitoring method based on multivariate statistical process control (MSPC) which is a well-known process monitoring algorithm and JD-SPE. This monitoring method is referred to as JD-SPE-r2. The proposed JD-SPE-Γ2 can detect a fault that can not detected by the conventional MSPC. The usefulness of the proposed methods is demonstrated through numerical examples.

DOI： 10.1109/ICCA.2017.8003187

Scopus

Language：Japanese  

DOI： 10.11499/sicejl.56.526

Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

J-GLOBAL

Event date： 2021.9

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2021.9

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2021.9

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2021.9

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2021.9

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2021

Language：English   Presentation type：Oral presentation (general)  

Event date： 2021

Language：English   Presentation type：Oral presentation (general)  

Other Link： https://dblp.uni-trier.de/rec/conf/apsipa/2021

Event date： 2020.10

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2020.10

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2020

Language：English   Presentation type：Oral presentation (general)  

Event date： 2020

Language：English   Presentation type：Oral presentation (general)  

Event date： 2019.12

Language：English   Presentation type：Oral presentation (general)  

Event date： 2019.11

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2019.9

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2019.7

Language：English   Presentation type：Oral presentation (general)  

Sleep apnea syndrome (SAS) is a prevalent disorder which causes daytime fatigue with the increased risk of lifestyle diseases. A large number of patients are undiagnosed and untreated partly because of the difficulty in performing its gold standard test, polysomnography (PSG). In this research, we propose a simple screening method utilizing heart rate variability (HRV) and long short-term memory (LSTM) which is a kind of neural network techniques. The result of applying this algorithm to clinical data demonstrates that it can discriminate between patients and healthy people with high sensitivity (100%) and specificity (100%).

Event date： 2019.6

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2019.6

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2019.6

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2019

Language：Japanese   Presentation type：Oral presentation (general)  

<p>Sleep apnea syndrome (SAS) is a prevalent disorder which causes daytime fatigue with increased risk of cardiovascular diseases. A large number of patients are undiagnosed and untreated partly because of the difficulty in performing its gold standard test, polysomnography (PSG). In this research, we propose a simple screening method utilizing heart rate variability (HRV) and long short-term memory (LSTM) which is a kind of the neural network techniques. The result of applying this algorithm to clinical data demonstrates that it can discriminate between patients and healthy people with sensitivity (100%) and specificity (100%).</p>

Event date： 2019

Language：English   Presentation type：Oral presentation (general)  

Event date： 2019

Language：English   Presentation type：Oral presentation (general)  

Event date： 2018.11

Language：English   Presentation type：Oral presentation (general)  

Event date： 2018.7

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2018.7

Language：English   Presentation type：Oral presentation (general)  

Event date： 2018.5

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2018.5

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2018.3

Language：English   Presentation type：Oral presentation (general)  

© 2018 IEEE. When players are excited by playing a video game, corresponding physiological responses such as sweating or changes in heart rate may appear. It is assumed that presenting physiological responses during game play to players in real-time alters their game experience even when they play the same game. Based on this assumption, this work investigated the effect of false heart rate (HR) feedback on game experience through experiments using a simple action game. Our experimental results indicated that false HR feedback not only prevented the players from becoming tired of the game but also enhanced players' experiences. In addition, a new game controller that can present HR information audibly and tactually was developed for realizing a game system based on false HR feedback.

Event date： 2018.3

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2018.3

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2018.2

Language：English   Presentation type：Oral presentation (general)  

© 2017 IEEE. In the manufacturing industry, it is extremely important to identify variables that affect product quality. Identifying variables which affect quality variables is called causal analysis. In batch processes, time-series data of process variables and the corresponding data of quality variables are generally acquired. Since causal analysis using the raw data needs a large computation load, it is often performed after compressing time-series process variables data into non-time-series feature variables data. Various causal analysis methods using such data have been developed, however, none have shown effective results in actual plants. In the present work, non-time-series kernel Granger causality (NTS-KGC) is proposed for causal analysis with non-time-series data of batch processes. This is a method that kernel Granger causality [1], which is used for causal analysis with time-series data in nonlinear systems, is expanded for causal analysis with non-time-series data. The validity of the proposed method is demonstrated through a numerical example of a nonlinear batch process. In addition, we conducted a case study of applying NTS-KGC to data obtained from a real steelmaking process. The results demonstrate that NTS-KGC is superior to other existing methods using the following indexes, i.e. variable influence on projection (VIP) of partial least squares (PLS), regression coefficients of PLS, and variable importance of Random Forest.

Event date： 2018.1

Language：English   Presentation type：Oral presentation (general)  

© 2018 Elsevier B.V. Epilepsy is a group of neurological disorders which is caused by excessive neuronal activities in cerebrum and characterized by recurrent seizures. A quarter of patients have intractable epilepsy and do not become seizure-free with medication. We are developing an implantable and wearable focal brain cooling system, which enables the patients to lead ordinary daily life. The system cools the epileptic focus, where the excessive neuronal activities begin, by delivering cold saline to a cranially implanted cooling device. In this research, we developed a whole system model through the first principles and animal experiments. The results of system design have shown that a cooling device with more complex channel structure achieves higher temperature uniformity in the brain with lower flow rate of saline. The optimal structure was derived by taking account of the trade-off between pressure drop and temperature uniformity. In addition, the results have demonstrated that the cooling duration is less than 10 minutes for the average temperature 2 mm below the cooling device (inside the brain) to reach 25 °C; it is short enough to cool the brain after seizure is predicted by existing electroencephalogram (EEG)-based algorithms. Moreover, the frequency of battery charging would be once in several days for most patients.

Event date： 2018.1

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2018

Language：Japanese   Presentation type：Oral presentation (general)  

&lt;p&gt;The present study aims to predict generalized seizures based on heart rate variability (HRV) analysis and multivariate statistical process control (MSPC). We applied the existing anomaly monitoring algorithm, i.e. HRV-based MSPC developed by Fujiwara et al. to 17 pre-ictal episodes and 74 inter-ictal episodes whose total length were about 63 hours. Consequently, the sensitivity and the false positive rate were 76.5% and 1.39 times per hour respectively, and the proportion of duration under false alarms was 5.96%. These results suggest that generalized seizures may be predicted by analyzing HRV. Based on the previous research, we hypothesize that the change in the autonomic nervous activity induce generalized epileptic seizures.&lt;/p&gt;

Event date： 2017.11

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2017.11

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2017.11

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2017.11

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2017.10

Language：English   Presentation type：Oral presentation (general)  

Event date： 2017.9

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2017.9

Language：English   Presentation type：Oral presentation (general)  

© 2017 IEEE. Objective: The present study proposes a cerebral infarction detection algorithm based on heart rate variability (HRV). Methods: It has been reported that infarction affects HRV. Therefore, infarction could be detected at an acute stage by monitoring HRV. This study uses multivariate statistical process control (MSPC), which is a well-known anomaly monitoring method. HRV data shortly after infarction onsets are collected by using the middle cerebral artery occlusion (MCAO) model in rats. This study prepares 11 MCAO-operated rats and 11 sham-operated rats. Three sham-operated rats' data are used for model construction of MSPC, and the other 19 rats' data are used for its validation. Results: The sensitivity and specificity of the proposed algorithm were 82 % and 75 %, respectively. Conclusion: An infarction onset could be detected at an acute stage by monitoring HRV.

Event date： 2017.9

Language：English   Presentation type：Oral presentation (general)  

© 2017 IEEE. Epilepsy is a group of diseases caused by excessive neuronal activities, and one-quarter of the patients do not become seizure-free by the existing treatments. The potential treatments include focal brain cooling, which aims to cool the region where the excessive neuronal activities begin. We are developing a focal brain cooling system. The system delivers cold saline to a cranially implanted cooling device. The outflow is cooled by a Peltier device and pumped for circulation. The Peltier device and the pump are activated only when a seizure is predicted. In this research, the length of time for cooling the brain was calculated with a computational fluid dynamics (CFD)-based model of the focal brain cooling system. As a result, it takes less than 10 minutes for the average temperature 2 mm below the cooling device to reach 25.0 °C. It is much shorter than the time from seizure prediction to seizure onset when an existing algorithm for prediction is used.

Event date： 2017.8

Language：English   Presentation type：Oral presentation (general)  

© 2017 IEEE. Although process monitoring is important for maintaining safety and product quality, it is difficult to understand process characteristics particularly when they are changing. Since the correlation among variables changes due to changes in process characteristics, process data visualization based on the correlation among variables helps process characteristic understanding. In the present work, a new correlation-based data visualization method is proposed by integrating joint decorrelation (JD) and stochastic proximity embedding (SPE). JD is a blind source separation (BSS) method that can separates sample based on the correlation, and SPE is a self-organizing algorithm that can map high-dimensional data to a two-dimensional plane. The proposed method, referred to as JD-SPE, separates samples based on the correlation using JD and the separated samples are visualized in the two-dimensional plane by SPE. Correlation matrices have to be constructed before sample separation for JD; however how to construct them is not clear. The present work also proposes a correlation matrix construction method for JD by using nearest correlation spectral clustering (NCSC), which is a correlation-based clustering method. In addition, a new process monitoring method based on multivariate statistical process control (MSPC) which is a well-known process monitoring algorithm and JD-SPE. This monitoring method is referred to as JD-SPE-r2. The proposed JD-SPE-Γ2 can detect a fault that can not detected by the conventional MSPC. The usefulness of the proposed methods is demonstrated through numerical examples.

Event date： 2017.5

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2017.1

Language：English   Presentation type：Oral presentation (general)  

Emotions of a person affect the person's performance in a task and so do emotions of a rescue dog that works after disasters. Hence, estimating emotions of a rescue dog by the handler can improve its performance and welfare. Emotions also appear in physiological signals such as heart rate variability (HRV). In fact, HRV has information of emotions in both cases of human and dogs. To make emotion estimation more practical, we proposed a method for emotion estimation from HRV of dogs and evaluated its performance using real data. The method classified positive, negative, and neutral emotions with 88% accuracy within each subject and 72% over all subjects. These accuracies are high enough for practical use in rescue dogs.

Event date： 2016

Language：English   Presentation type：Oral presentation (general)  

If player condition during video game playing could be measured in real time, it would become possible to develop a new game interaction system. Since heart rate (HR) has been used for various psychological condition estimation, it can be used for player condition estimation. In the present work, a new game controller that can measure player HR without letting the player be conscious of the controller based on a photoplethysmogram (PPG) was developed. The experiment result demonstrated that the newly developed game controller could measure the player HR with sufficiently high accuracy.

Event date： 2010

Language：English   Presentation type：Oral presentation (general)  

The current issues concerning soft-sensors are how to cope with changes in process characteristics and how to cope with parallelized, slightly different, multiple processes. To make soft-sensors adaptive and flexible, the development of practical design techniques, instead of impracticable ideas, is crucial; this is the motivation of the present research. In practice, it is difficult to successfully apply a single soft-sensor to parallelized production devices due to their individual difference. Since the individual difference is expressed as difference of the correlation among variables, it is useful to classify samples into multiple clusters according to the correlation in order to adopt a multi-model approach. In the present work, a new correlation-based clustering method, referred to as NC-spectral clustering, is proposed by integrating the nearest correlation (NC) method and spectral clustering. The NC method can detect samples that are similar to the query from the viewpoint of the correlation. In the proposed method, the NC method is used for constructing the weighted graph that expresses the correlation-based similarities between samples and the constructed graph is partitioned by using spectral clustering. In addition, a new soft-sensor design method is proposed on the basis of the proposed NC-spectral clustering. The superiority of the proposed method over conventional methods is demonstrated through a numerical example and a case study of parallelized batch processes. © 2009 IFAC.

Event date： 2009

Language：English   Presentation type：Oral presentation (general)  

Other Link： http://orcid.org/0000-0002-2325-1043

Language：Japanese   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Language：Japanese   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Language：English   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Language：English   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Language：English   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Language：English   Presentation type：Symposium, workshop panel (nominated)  

Language：Japanese   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Language：English   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Language：Japanese   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Language：English   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Language：Japanese   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Language：English   Presentation type：Oral presentation (invited, special)  

Language：Japanese   Presentation type：Symposium, workshop panel (nominated)  

Language：Japanese   Presentation type：Symposium, workshop panel (nominated)  

Language：English   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Language：English   Presentation type：Symposium, workshop panel (nominated)  

Language：Japanese   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Language：Japanese   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Language：Japanese   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Language：Japanese   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Language：Japanese   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Language：Japanese   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Applicant：名古屋大学

Application no：2020-097152  Date applied：2020.6

Country of applicant：Domestic  

Applicant：国立大学法人京都大学

Application no：2019-23217  Date applied：2019.6

Country of applicant：Domestic  

Application no：特願2019-023217  Date applied：2019.2

Applicant：京都大学

Application no：特願2018-181414  Date applied：2018.9

Applicant：京都大学

Application no：特願2018-90592  Date applied：2018.5

Applicant：京都大学

Application no：特願2015-101782  Date applied：2015.5

Announcement no：特開2016-214491  Date announced：2016.12

Patent/Registration no：特許6691334  Date registered：2020.4  Date issued：2020.4

Applicant：京都大学

Application no：特願2014-114093  Date applied：2014.6

Announcement no：特開2015-226696  Date announced：2015.12

Patent/Registration no：特許6375496  Date registered：2018.8  Date issued：2018.8

Applicant：日本電信電話株式会社

Application no：特願2012-152618  Date applied：2012.7

Announcement no：特開2014-17605  Date announced：2014.1

Country of applicant：Domestic  

Applicant：日本電信電話株式会社

Application no：特願2011-240543  Date applied：2011.11

Announcement no：特開2013-99143  Date announced：2013.5

Patent/Registration no：特許5661594  Date registered：2014.12 

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Applicant：京都大学

Application no：特願2009-151745  Date applied：2009.6

Announcement no：特開2011-008562  Date announced：2011.1

Patent/Registration no：特許5457737  Date registered：2014.1 

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Applicant：京都大学

Announcement no：特開2008-517936  Date announced：2007.5

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Announcement no：特開2006-323523  Date announced：2006.11

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