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Pan Z, Liao S, Sun W, Zhou H, Lin S, Chen D, Jiang S, Long H, Fan J, Deng F, Zhang W, Chen B, Wang J, Huang Y, Li J, Chen Y. Screening and early warning system for chronic obstructive pulmonary disease with obstructive sleep apnoea based on the medical Internet of Things in three levels of healthcare: protocol for a prospective, multicentre, observational cohort study. BMJ Open 2024; 14:e075257. [PMID: 38418236 PMCID: PMC10910414 DOI: 10.1136/bmjopen-2023-075257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 02/12/2024] [Indexed: 03/01/2024] Open
Abstract
INTRODUCTION Chronic obstructive pulmonary disease (COPD) and obstructive sleep apnoea (OSA) are prevalent respiratory diseases in China and impose significant burdens on the healthcare system. Moreover, the co-occurrence of COPD and OSA exacerbates clinical outcomes significantly. However, comprehensive epidemiological investigations in China remain scarce, and the defining characteristics of the population affected by COPD and OSA, alongside their intrinsic relationship, remain ambiguous. METHODS AND ANALYSIS We present a protocol for a prospective, multicentre, observational cohort study based on a digital health management platform across three different healthcare tiers in five sites among Chinese patients with COPD. The study aims to establish predicative models to identify OSA among patients with COPD and to predict the prognosis of overlap syndrome (OS) and acute exacerbations of COPD through the Internet of Things (IoT). Moreover, it aims to evaluate the feasibility, effectiveness and cost-effectiveness of IoT in managing chronic diseases within clinical settings. Participants will undergo baseline assessment, physical examination and nocturnal oxygen saturation measuring. Specific questionnaires screening for OSA will also be administered. Diagnostic lung function tests and polysomnography will be performed to confirm COPD and OSA, respectively. All patients will undergo scheduled follow-ups for 12 months to record the changes in symptoms, lung functions and quality of life. Primary outcomes include the prevalence and characteristics of OS, while secondary outcomes encompass OS prognosis and the feasibility of the management model in clinical contexts. A total of 682 patients with COPD will be recruited over 12-24 months. ETHICS AND DISSEMINATION The study has been approved by Peking University Third Hospital, and all study participants will provide written informed consent. Study results will be published in an appropriate journal and presented at national and international conferences, as well as relevant social media and various stakeholder engagement activities. TRIAL REGISTRATION NUMBER NCT04833725.
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Affiliation(s)
- Zihan Pan
- Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, China
- General Practice Medicine, Peking University First Hospital, Beijing, China
| | - Sha Liao
- Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, China
| | - Wanlu Sun
- Department of Pulmonary and Critical Care Medicine, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Beijing, China
| | - Haoyi Zhou
- School of Software, Beihang University, Beijing, China
| | - Shuo Lin
- Air Liquide Healthcare (Beijing), Beijing, China
| | - Dian Chen
- Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, China
| | - Simin Jiang
- Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, China
| | - Huanyu Long
- Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, China
| | - Jing Fan
- Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, China
| | - Furong Deng
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
| | - Wenlou Zhang
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
| | - Baiqi Chen
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
| | - Junyi Wang
- Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, China
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
| | - Yongwei Huang
- Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, China
- Sleep Monitoring Center, Peking University Third Hospital, Beijing, China
| | - Jianxin Li
- School of Computer Science and Engineering, Beihang University, Beijing, China
| | - Yahong Chen
- Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, China
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Arslan RS. Sleep disorder and apnea events detection framework with high performance using two-tier learning model design. PeerJ Comput Sci 2023; 9:e1554. [PMID: 37810361 PMCID: PMC10557519 DOI: 10.7717/peerj-cs.1554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 08/04/2023] [Indexed: 10/10/2023]
Abstract
Sleep apnea is defined as a breathing disorder that affects sleep. Early detection of sleep apnea helps doctors to take intervention for patients to prevent sleep apnea. Manually making this determination is a time-consuming and subjectivity problem. Therefore, many different methods based on polysomnography (PSG) have been proposed and applied to detect this disorder. In this study, a unique two-layer method is proposed, in which there are four different deep learning models in the deep neural network (DNN), gated recurrent unit (GRU), recurrent neural network (RNN), RNN-based-long term short term memory (LSTM) architecture in the first layer, and a machine learning-based meta-learner (decision-layer) in the second layer. The strategy of making a preliminary decision in the first layer and verifying/correcting the results in the second layer is adopted. In the training of this architecture, a vector consisting of 23 features consisting of snore, oxygen saturation, arousal and sleep score data is used together with PSG data. A dataset consisting of 50 patients, both children and adults, is prepared. A number of pre-processing and under-sampling applications have been made to eliminate the problem of unbalanced classes. Proposed method has an accuracy of 95.74% and 99.4% in accuracy of apnea detection (apnea, hypopnea and normal) and apnea types detection (central, mixed and obstructive), respectively. Experimental results demonstrate that patient-independent consistent results can be produced with high accuracy. This robust model can be considered as a system that will help in the decisions of sleep clinics where it is expected to detect sleep disorders in detail with high performance.
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Tyagi PK, Agarwal D. Systematic review of automated sleep apnea detection based on physiological signal data using deep learning algorithm: a meta-analysis approach. Biomed Eng Lett 2023; 13:293-312. [PMID: 37519869 PMCID: PMC10382448 DOI: 10.1007/s13534-023-00297-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 06/10/2023] [Accepted: 06/18/2023] [Indexed: 08/01/2023] Open
Abstract
Sleep apnea (SLA) is a respiratory-related sleep disorder that affects a major proportion of the population. The gold standard in sleep testing, polysomnography, is costly, inconvenient, and unpleasant, and it requires a skilled professional to score. Multiple researchers have suggested and developed automated scoring processes with less detectors and automated classification algorithms to resolve these problems. An automatic detection system will allow for a high diagnosis rate and the analysis of additional patients. Deep learning (DL) is achieving high priority due to the availability of databases and recently developed methods. As the most up-and-coming technique for classification and generative tasks, DL has shown its significant potential in 2-dimensional clinical image processing studies. However, physiological information collected as 1-dimensional data has yet to be effectively extracted from this new approach to achieve the needed medical goals. So, in this study, we review the most recent studies in the field of DL applied to physiological data based on pulse oxygen saturation, electrocardiogram, airflow, and sound signal. A total of 47 articles from different journals and publishing houses that were published between 2012 and 2022 were identified. The primary objective of this work is to perform a comprehensive analysis to analyze, classify, and compare the main characteristics of deep-learning algorithms applied in physiological data processing for SLA detection. Overall, our analysis provides comprehensive and detailed information for researchers looking to add to this field. The data input source, objective, DL network, training framework, and database references are the critical factors of the DL approach examined. These are the most critical variables that influence system performance. We categorized the relevant research studies in physiological sensor data analysis using the DL approach based on (1) Physiological sensor data aspects, like signal types, sampling frequency, and window size; and (2) DL model perspectives, such as learning structure and input data types. Supplementary Information The online version contains supplementary material available at 10.1007/s13534-023-00297-5.
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Affiliation(s)
- Praveen Kumar Tyagi
- Department of ECE, Maulana Azad National Institute of Technology, Bhopal, 462003 India
| | - Dheeraj Agarwal
- Department of ECE, Maulana Azad National Institute of Technology, Bhopal, 462003 India
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Bazoukis G, Bollepalli SC, Chung CT, Li X, Tse G, Bartley BL, Batool-Anwar S, Quan SF, Armoundas AA. Application of artificial intelligence in the diagnosis of sleep apnea. J Clin Sleep Med 2023; 19:1337-1363. [PMID: 36856067 PMCID: PMC10315608 DOI: 10.5664/jcsm.10532] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 02/21/2023] [Accepted: 02/21/2023] [Indexed: 03/02/2023]
Abstract
STUDY OBJECTIVES Machine learning (ML) models have been employed in the setting of sleep disorders. This review aims to summarize the existing data about the role of ML techniques in the diagnosis, classification, and treatment of sleep-related breathing disorders. METHODS A systematic search in Medline, EMBASE, and Cochrane databases through January 2022 was performed. RESULTS Our search strategy revealed 132 studies that were included in the systematic review. Existing data show that ML models have been successfully used for diagnostic purposes. Specifically, ML models showed good performance in diagnosing sleep apnea using easily obtained features from the electrocardiogram, pulse oximetry, and sound signals. Similarly, ML showed good performance for the classification of sleep apnea into obstructive and central categories, as well as predicting apnea severity. Existing data show promising results for the ML-based guided treatment of sleep apnea. Specifically, the prediction of outcomes following surgical treatment and optimization of continuous positive airway pressure therapy can be guided by ML models. CONCLUSIONS The adoption and implementation of ML in the field of sleep-related breathing disorders is promising. Advancements in wearable sensor technology and ML models can help clinicians predict, diagnose, and classify sleep apnea more accurately and efficiently. CITATION Bazoukis G, Bollepalli SC, Chung CT, et al. Application of artificial intelligence in the diagnosis of sleep apnea. J Clin Sleep Med. 2023;19(7):1337-1363.
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Affiliation(s)
- George Bazoukis
- Department of Cardiology, Larnaca General Hospital, Larnaca, Cyprus
- Department of Basic and Clinical Sciences, University of Nicosia Medical School, Nicosia, Cyprus
| | | | - Cheuk To Chung
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, China-UK Collaboration, Hong Kong
| | - Xinmu Li
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular disease, Department of Cardiology, Tianjin Institute of Cardiology, the Second Hospital of Tianjin Medical University, Tianjin, China
| | - Gary Tse
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, China-UK Collaboration, Hong Kong
- Kent and Medway Medical School, Canterbury, Kent, United Kingdom
| | - Bethany L. Bartley
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Salma Batool-Anwar
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Stuart F. Quan
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts
- Asthma and Airway Disease Research Center, University of Arizona College of Medicine, Tucson, Arizona
| | - Antonis A. Armoundas
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts
- Broad Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts
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Kuo CF, Tsai CY, Cheng WH, Hs WH, Majumdar A, Stettler M, Lee KY, Kuan YC, Feng PH, Tseng CH, Chen KY, Kang JH, Lee HC, Wu CJ, Liu WT. Machine learning approaches for predicting sleep arousal response based on heart rate variability, oxygen saturation, and body profiles. Digit Health 2023; 9:20552076231205744. [PMID: 37846406 PMCID: PMC10576931 DOI: 10.1177/20552076231205744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/18/2023] [Indexed: 10/18/2023] Open
Abstract
Objective Obstructive sleep apnea is a global health concern, and several tools have been developed to screen its severity. However, most tools focus on respiratory events instead of sleep arousal, which can also affect sleep efficiency. This study employed easy-to-measure parameters-namely heart rate variability, oxygen saturation, and body profiles-to predict arousal occurrence. Methods Body profiles and polysomnography recordings were collected from 659 patients. Continuous heart rate variability and oximetry measurements were performed and then labeled based on the presence of sleep arousal. The dataset, comprising five body profiles, mean heart rate, six heart rate variability, and five oximetry variables, was then split into 80% training/validation and 20% testing datasets. Eight machine learning approaches were employed. The model with the highest accuracy, area under the receiver operating characteristic curve, and area under the precision recall curve values in the training/validation dataset was applied to the testing dataset and to determine feature importance. Results InceptionTime, which exhibited superior performance in predicting sleep arousal in the training dataset, was used to classify the testing dataset and explore feature importance. In the testing dataset, InceptionTime achieved an accuracy of 76.21%, an area under the receiver operating characteristic curve of 84.33%, and an area under the precision recall curve of 86.28%. The standard deviations of time intervals between successive normal heartbeats and the square roots of the means of the squares of successive differences between normal heartbeats were predominant predictors of arousal occurrence. Conclusions The established models can be considered for screening sleep arousal occurrence or integrated in wearable devices for home-based sleep examination.
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Affiliation(s)
- Chih-Fan Kuo
- School of Medicine, China Medical University, Taichung City, Taichung, Taiwan
- Artificial Intelligence Center, China Medical University Hospital, Taichung, Taiwan
- Department of Medical Education, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Cheng-Yu Tsai
- Department of Civil and Environmental Engineering, Imperial College London, London, UK
- Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Wun-Hao Cheng
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Respiratory Therapy, Division of Pulmonary Medicine, Department of Internal Medicine, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Wen-Hua Hs
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Arnab Majumdar
- Department of Civil and Environmental Engineering, Imperial College London, London, UK
| | - Marc Stettler
- Department of Civil and Environmental Engineering, Imperial College London, London, UK
| | - Kang-Yun Lee
- Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
- Division of Pulmonary Medicine, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei City, Taiwan
| | - Yi-Chun Kuan
- Sleep Center, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
- Department of Neurology, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
- Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan
| | - Po-Hao Feng
- Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
- Division of Pulmonary Medicine, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei City, Taiwan
| | - Chien-Hua Tseng
- Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
- Division of Pulmonary Medicine, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei City, Taiwan
| | - Kuan-Yuan Chen
- Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Jiunn-Horng Kang
- Research Center of Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan
- Graduate Institute of Nanomedicine and Medical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei, Taiwan
| | - Hsin-Chien Lee
- Department of Psychiatry, Taipei Medical University Hospital, Taipei, Taiwan
| | - Cheng-Jung Wu
- Department of Otolaryngology, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
| | - Wen-Te Liu
- Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Sleep Center, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
- Research Center of Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan
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Alugubelli N, Abuissa H, Roka A. Wearable Devices for Remote Monitoring of Heart Rate and Heart Rate Variability-What We Know and What Is Coming. SENSORS (BASEL, SWITZERLAND) 2022; 22:8903. [PMID: 36433498 PMCID: PMC9695982 DOI: 10.3390/s22228903] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 09/27/2022] [Accepted: 11/15/2022] [Indexed: 05/26/2023]
Abstract
Heart rate at rest and exercise may predict cardiovascular risk. Heart rate variability is a measure of variation in time between each heartbeat, representing the balance between the parasympathetic and sympathetic nervous system and may predict adverse cardiovascular events. With advances in technology and increasing commercial interest, the scope of remote monitoring health systems has expanded. In this review, we discuss the concepts behind cardiac signal generation and recording, wearable devices, pros and cons focusing on accuracy, ease of application of commercial and medical grade diagnostic devices, which showed promising results in terms of reliability and value. Incorporation of artificial intelligence and cloud based remote monitoring have been evolving to facilitate timely data processing, improve patient convenience and ensure data security.
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Affiliation(s)
| | | | - Attila Roka
- Division of Cardiology, Creighton University and CHI Health, 7500 Mercy Rd, Omaha, NE 68124, USA
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Xu S, Faust O, Seoni S, Chakraborty S, Barua PD, Loh HW, Elphick H, Molinari F, Acharya UR. A review of automated sleep disorder detection. Comput Biol Med 2022; 150:106100. [PMID: 36182761 DOI: 10.1016/j.compbiomed.2022.106100] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 09/04/2022] [Accepted: 09/12/2022] [Indexed: 12/22/2022]
Abstract
Automated sleep disorder detection is challenging because physiological symptoms can vary widely. These variations make it difficult to create effective sleep disorder detection models which support hu-man experts during diagnosis and treatment monitoring. From 2010 to 2021, authors of 95 scientific papers have taken up the challenge of automating sleep disorder detection. This paper provides an expert review of this work. We investigated whether digital technology and Artificial Intelligence (AI) can provide automated diagnosis support for sleep disorders. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines during the content discovery phase. We compared the performance of proposed sleep disorder detection methods, involving differ-ent datasets or signals. During the review, we found eight sleep disorders, of which sleep apnea and insomnia were the most studied. These disorders can be diagnosed using several kinds of biomedical signals, such as Electrocardiogram (ECG), Polysomnography (PSG), Electroencephalogram (EEG), Electromyogram (EMG), and snore sound. Subsequently, we established areas of commonality and distinctiveness. Common to all reviewed papers was that AI models were trained and tested with labelled physiological signals. Looking deeper, we discovered that 24 distinct algorithms were used for the detection task. The nature of these algorithms evolved, before 2017 only traditional Machine Learning (ML) was used. From 2018 onward, both ML and Deep Learning (DL) methods were used for sleep disorder detection. The strong emergence of DL algorithms has considerable implications for future detection systems because these algorithms demand significantly more data for training and testing when compared with ML. Based on our review results, we suggest that both type and amount of labelled data is crucial for the design of future sleep disorder detection systems because this will steer the choice of AI algorithm which establishes the desired decision support. As a guiding principle, more labelled data will help to represent the variations in symptoms. DL algorithms can extract information from these larger data quantities more effectively, therefore; we predict that the role of these algorithms will continue to expand.
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Affiliation(s)
- Shuting Xu
- Cogninet Brain Team, Sydney, NSW, 2010, Australia
| | - Oliver Faust
- Anglia Ruskin University, East Rd, Cambridge CB1 1PT, UK.
| | - Silvia Seoni
- Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - Subrata Chakraborty
- School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW, 2351, Australia; Centre for Advanced Modelling and Geospatial Lnformation Systems (CAMGIS), Faculty of Engineer and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - Prabal Datta Barua
- Cogninet Brain Team, Sydney, NSW, 2010, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia; School of Business (Information System), University of Southern Queensland, Australia
| | - Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, 463 Clementi Road, 599494, Singapore
| | | | - Filippo Molinari
- Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - U Rajendra Acharya
- School of Business (Information System), University of Southern Queensland, Australia; School of Science and Technology, Singapore University of Social Sciences, 463 Clementi Road, 599494, Singapore; Department of Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan.
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Iwasaki A, Fujiwara K, Nakayama C, Sumi Y, Kano M, Nagamoto T, Kadotani H. R-R interval-based sleep apnea screening by a recurrent neural network in a large clinical polysomnography dataset. Clin Neurophysiol 2022; 139:80-89. [DOI: 10.1016/j.clinph.2022.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 03/10/2022] [Accepted: 04/12/2022] [Indexed: 11/03/2022]
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Nagata S, Fujiwara K, Kuga K, Ozaki H. Prediction of GABA receptor antagonist-induced convulsion in cynomolgus monkeys by combining machine learning and heart rate variability analysis. J Pharmacol Toxicol Methods 2021; 112:107127. [PMID: 34619314 DOI: 10.1016/j.vascn.2021.107127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 09/30/2021] [Accepted: 09/30/2021] [Indexed: 12/19/2022]
Abstract
Drug-induced convulsion is a severe adverse event; however, no useful biomarkers for it have been discovered. We propose a new method for predicting drug-induced convulsions in monkeys based on heart rate variability (HRV) and a machine learning technique. Because autonomic nervous activities are altered around the time of a convulsion and such alterations affect HRV, they may be predicted by monitoring HRV. In the proposed method, anomalous changes in multiple HRV parameters are monitored by means of a convulsion prediction model, and convulsion alarms are issued when abnormal changes in HRV are detected. The convulsion prediction model is constructed based on multivariate statistical process control (MSPC), a well-known anomaly detection algorithm in machine learning. In this study, HRV data were collected from four cynomolgus monkeys administered with multiple doses of pentylenetetrazol (PTZ) and picrotoxin (PTX), which are GABA receptor antagonists, as convulsant agents. In addition, low doses of pilocarpine (PILO) were administered as a negative control. Twelve HRV parameters in three hours after drug administration were monitored by means of the prediction model. The number and duration of convulsion alarms from HRV increased at medium and high doses of PTZ and PTX (1/3 or 1/4 of convulsion dose). On the other hand, the frequency of convulsion alarms did not increase with PILO. Although vomiting was observed in all drugs, convulsion alarms were not associated with the vomiting. Thus, convulsion alarms can be used as a biomarker for convulsions induced by GABA receptor antagonists.
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Affiliation(s)
- Shoya Nagata
- Department of Material Process Engineering, Nagoya University, Nagoya, Japan
| | - Koichi Fujiwara
- Department of Material Process Engineering, Nagoya University, Nagoya, Japan.
| | - Kazuhiro Kuga
- Drug Safety Research and Evaluation, Takeda Pharmaceutical Company Ltd., Kanagawa, Japan
| | - Harushige Ozaki
- Drug Safety Research and Evaluation, Takeda Pharmaceutical Company Ltd., Kanagawa, Japan
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Fujiwara K, Miyatani S, Goda A, Miyajima M, Sasano T, Kano M. Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability Analysis. SENSORS 2021; 21:s21093235. [PMID: 34067051 PMCID: PMC8125061 DOI: 10.3390/s21093235] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 04/27/2021] [Accepted: 05/02/2021] [Indexed: 12/19/2022]
Abstract
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.
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Affiliation(s)
- Koichi Fujiwara
- Department of Material Process Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8601, Japan
- Correspondence:
| | - Shota Miyatani
- Department of Systems Science, Kyoto University, Kyoto 606-8501, Japan; (S.M.); (A.G.); (M.K.)
| | - Asuka Goda
- Department of Systems Science, Kyoto University, Kyoto 606-8501, Japan; (S.M.); (A.G.); (M.K.)
| | - Miho Miyajima
- Department of Liaison Psychiatry and Palliative Medicine, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (M.M.); (T.S.)
| | - Tetsuo Sasano
- Department of Liaison Psychiatry and Palliative Medicine, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (M.M.); (T.S.)
| | - Manabu Kano
- Department of Systems Science, Kyoto University, Kyoto 606-8501, Japan; (S.M.); (A.G.); (M.K.)
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