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Liu MH, Chien SY, Wu YL, Sun TH, Huang CS, Hsu KC, Hang LW. EfficientNet-based machine learning architecture for sleep apnea identification in clinical single-lead ECG signal data sets. Biomed Eng Online 2024; 23:57. [PMID: 38902671 PMCID: PMC11188209 DOI: 10.1186/s12938-024-01252-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 06/03/2024] [Indexed: 06/22/2024] Open
Abstract
OBJECTIVE Our objective was to create a machine learning architecture capable of identifying obstructive sleep apnea (OSA) patterns in single-lead electrocardiography (ECG) signals, exhibiting exceptional performance when utilized in clinical data sets. METHODS We conducted our research using a data set consisting of 1656 patients, representing a diverse demographic, from the sleep center of China Medical University Hospital. To detect apnea ECG segments and extract apnea features, we utilized the EfficientNet and some of its layers, respectively. Furthermore, we compared various training and data preprocessing techniques to enhance the model's prediction, such as setting class and sample weights or employing overlapping and regular slicing. Finally, we tested our approach against other literature on the Apnea-ECG database. RESULTS Our research found that the EfficientNet model achieved the best apnea segment detection using overlapping slicing and sample-weight settings, with an AUC of 0.917 and an accuracy of 0.855. For patient screening with AHI > 30, we combined the trained model with XGBoost, leading to an AUC of 0.975 and an accuracy of 0.928. Additional tests using PhysioNet data showed that our model is comparable in performance to existing models regarding its ability to screen OSA levels. CONCLUSIONS Our suggested architecture, coupled with training and preprocessing techniques, showed admirable performance with a diverse demographic dataset, bringing us closer to practical implementation in OSA diagnosis. Trial registration The data for this study were collected retrospectively from the China Medical University Hospital in Taiwan with approval from the institutional review board CMUH109-REC3-018.
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Affiliation(s)
- Meng-Hsuan Liu
- Artificial Intelligence Center, China Medical University Hospital, No. 2, Yude Rd, North Dist, Taichung, Taiwan
| | - Shang-Yu Chien
- Artificial Intelligence Center, China Medical University Hospital, No. 2, Yude Rd, North Dist, Taichung, Taiwan
| | - Ya-Lun Wu
- Artificial Intelligence Center, China Medical University Hospital, No. 2, Yude Rd, North Dist, Taichung, Taiwan
| | - Ting-Hsuan Sun
- Artificial Intelligence Center, China Medical University Hospital, No. 2, Yude Rd, North Dist, Taichung, Taiwan
| | - Chun-Sen Huang
- Sleep Medicine Center, Department of Pulmonary and Critical Care Medicine, China Medical University Hospital, No. 2, Yude Rd., North Dist, Taichung, Taiwan
| | - Kai-Cheng Hsu
- Artificial Intelligence Center, China Medical University Hospital, No. 2, Yude Rd, North Dist, Taichung, Taiwan.
- School of Medicine, China Medical University, Taichung, Taiwan.
- Neuroscience and Brain Disease Center, China Medical University, Taichung, Taiwan.
- Department of Neurology, China Medical University Hospital, Taichung, Taiwan.
| | - Liang-Wen Hang
- Sleep Medicine Center, Department of Pulmonary and Critical Care Medicine, China Medical University Hospital, No. 2, Yude Rd., North Dist, Taichung, Taiwan.
- Department of Respiratory Therapy, College of Health Care, China, Medical University Hospital, Taichung, Taiwan.
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Kim S, Choi HS, Kim D, Kim M, Lee SY, Kim JK, Kim Y, Lee WH. A Comprehensive Study on a Deep-Learning-Based Electrocardiography Analysis for Estimating the Apnea-Hypopnea Index. Diagnostics (Basel) 2024; 14:1134. [PMID: 38893660 PMCID: PMC11171733 DOI: 10.3390/diagnostics14111134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 05/24/2024] [Accepted: 05/28/2024] [Indexed: 06/21/2024] Open
Abstract
This study introduces a deep-learning-based automatic sleep scoring system to detect sleep apnea using a single-lead electrocardiography (ECG) signal, focusing on accurately estimating the apnea-hypopnea index (AHI). Unlike other research, this work emphasizes AHI estimation, crucial for the diagnosis and severity evaluation of sleep apnea. The suggested model, trained on 1465 ECG recordings, combines the deep-shallow fusion network for sleep apnea detection network (DSF-SANet) and gated recurrent units (GRUs) to analyze ECG signals at 1-min intervals, capturing sleep-related respiratory disturbances. Achieving a 0.87 correlation coefficient with actual AHI values, an accuracy of 0.82, an F1 score of 0.71, and an area under the receiver operating characteristic curve of 0.88 for per-segment classification, our model was effective in identifying sleep-breathing events and estimating the AHI, offering a promising tool for medical professionals.
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Affiliation(s)
- Seola Kim
- Ziovision Inc., Chuncheon 24341, Republic of Korea; (S.K.); (H.-S.C.); (D.K.); (M.K.)
| | - Hyun-Soo Choi
- Ziovision Inc., Chuncheon 24341, Republic of Korea; (S.K.); (H.-S.C.); (D.K.); (M.K.)
- Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
| | - Dohyun Kim
- Ziovision Inc., Chuncheon 24341, Republic of Korea; (S.K.); (H.-S.C.); (D.K.); (M.K.)
- Department of Computer and Communications Engineering, Kangwon National University, Chuncheon 24341, Republic of Korea
| | - Minkyu Kim
- Ziovision Inc., Chuncheon 24341, Republic of Korea; (S.K.); (H.-S.C.); (D.K.); (M.K.)
| | - Seo-Young Lee
- Department of Neurology, Kangwon National University Hospital, College of Medicine, Kangwon National University, Chuncheon 24289, Republic of Korea;
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Kangwon National University, Chuncheon 24341, Republic of Korea;
| | - Jung-Kyeom Kim
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Kangwon National University, Chuncheon 24341, Republic of Korea;
| | - Yoon Kim
- Department of Computer Science and Engineering, Kangwon National University, Chuncheon 24341, Republic of Korea;
| | - Woo Hyun Lee
- Department of Otolaryngology, Kangwon National University Hospital, College of Medicine, Kangwon National University, Chuncheon 24289, Republic of Korea
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Lin Y, Zhang H, Wu W, Gao X, Chao F, Lin J. Wavelet transform and deep learning-based obstructive sleep apnea detection from single-lead ECG signals. Phys Eng Sci Med 2024; 47:119-133. [PMID: 37982985 DOI: 10.1007/s13246-023-01346-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 10/12/2023] [Indexed: 11/21/2023]
Abstract
Sleep apnea is a common sleep disorder. Traditional testing and diagnosis heavily rely on the expertise of physicians, as well as analysis and statistical interpretation of extensive sleep testing data, resulting in time-consuming and labor-intensive processes. To address the problems of complex feature extraction, data imbalance, and low model capacity, we proposed an automatic sleep apnea classification model (CA-EfficientNet) based on the wavelet transform, a lightweight neural network, and a coordinated attention mechanism. The signal is converted into a time-frequency image by wavelet transform and put into the proposed model for classification. The effects of input time window, wavelet transform type and data balancing on the classification performance are considered, and a cost-sensitive algorithm is introduced to more accurately distinguish between normal and abnormal breathing events. PhysioNet apnea ECG database was used for training and evaluation. The 3-min Frequency B-Spline wavelets transform of ECG signal was carried out, and Dice Loss was used to train the classification model of sleep breathing. The classification accuracy was 93.44%, sensitivity was 88.9%, specificity was 96.2% and most indexes were better than other related work.
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Affiliation(s)
- Yuxing Lin
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, 361024, China
| | - Hongyi Zhang
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, 361024, China.
| | - Wanqing Wu
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, 510006, China
| | - Xingen Gao
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, 361024, China.
| | - Fei Chao
- School of Informatics, Xiamen University, Xiamen, 36100, China
| | - Juqiang Lin
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, 361024, China
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4
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Espinosa MA, Ponce P, Molina A, Borja V, Torres MG, Rojas M. Advancements in Home-Based Devices for Detecting Obstructive Sleep Apnea: A Comprehensive Study. SENSORS (BASEL, SWITZERLAND) 2023; 23:9512. [PMID: 38067885 PMCID: PMC10708697 DOI: 10.3390/s23239512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 11/24/2023] [Accepted: 11/25/2023] [Indexed: 12/18/2023]
Abstract
Obstructive Sleep Apnea (OSA) is a respiratory disorder characterized by frequent breathing pauses during sleep. The apnea-hypopnea index is a measure used to assess the severity of sleep apnea and the hourly rate of respiratory events. Despite numerous commercial devices available for apnea diagnosis and early detection, accessibility remains challenging for the general population, leading to lengthy wait times in sleep clinics. Consequently, research on monitoring and predicting OSA has surged. This comprehensive paper reviews devices, emphasizing distinctions among representative apnea devices and technologies for home detection of OSA. The collected articles are analyzed to present a clear discussion. Each article is evaluated according to diagnostic elements, the implemented automation level, and the derived level of evidence and quality rating. The findings indicate that the critical variables for monitoring sleep behavior include oxygen saturation (oximetry), body position, respiratory effort, and respiratory flow. Also, the prevalent trend is the development of level IV devices, measuring one or two signals and supported by prediction software. Noteworthy methods showcasing optimal results involve neural networks, deep learning, and regression modeling, achieving an accuracy of approximately 99%.
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Affiliation(s)
- Miguel A. Espinosa
- Institute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Mexico City 14380, Mexico; (M.A.E.); (M.R.)
| | - Pedro Ponce
- Institute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Mexico City 14380, Mexico; (M.A.E.); (M.R.)
| | - Arturo Molina
- Institute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Mexico City 14380, Mexico; (M.A.E.); (M.R.)
| | - Vicente Borja
- Faculty of Engineering, Universidad Nacional Autonoma de Mexico, Mexico City 04510, Mexico;
| | - Martha G. Torres
- Sleep Medicine Unit, Instituto Nacional de Enfermedades Respiratorias Ismael Cosio Villegas, Mexico City 14080, Mexico;
| | - Mario Rojas
- Institute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Mexico City 14380, Mexico; (M.A.E.); (M.R.)
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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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Xiong P, Yang L, Zhang J, Xu J, Yang J, Wang H, Liu X. Detection of inferior myocardial infarction based on multi branch hybrid network. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
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7
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Chen Y, Yang S, Li H, Wang L, Wang B. Prediction of Sleep Apnea Events Using a CNN-Transformer Network and Contactless Breathing Vibration Signals. Bioengineering (Basel) 2023; 10:746. [PMID: 37508773 PMCID: PMC10376604 DOI: 10.3390/bioengineering10070746] [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: 05/08/2023] [Revised: 06/08/2023] [Accepted: 06/15/2023] [Indexed: 07/30/2023] Open
Abstract
It is estimated that globally 425 million subjects have moderate to severe obstructive sleep apnea (OSA). The accurate prediction of sleep apnea events can offer insight into the development of treatment therapies. However, research related to this prediction is currently limited. We developed a covert framework for the prediction of sleep apnea events based on low-frequency breathing-induced vibrations obtained from piezoelectric sensors. A CNN-transformer network was utilized to efficiently extract local and global features from respiratory vibration signals for accurate prediction. Our study involved overnight recordings of 105 subjects. In five-fold cross-validation, we achieved an accuracy of 85.9% and an F1 score of 85.8%, which are 3.5% and 5.3% higher than the best-performed classical model, respectively. Additionally, in leave-one-out cross-validation, 2.3% and 3.8% improvements are observed, respectively. Our proposed CNN-transformer model is effective in the prediction of sleep apnea events. Our framework can thus provide a new perspective for improving OSA treatment modes and clinical management.
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Affiliation(s)
- Yuhang Chen
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Shuchen Yang
- Shanghai Yueyang Medtech Co., Shanghai 200131, China
| | - Huan Li
- Department of Sleep Medical Center, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Road, Beijing 100029, China
- Department of Ultrasound, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai 200092, China
| | - Lirong Wang
- School of Electronics and Information Technology, Soochow University, Suzhou 215006, China
| | - Bidou Wang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
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Yue H, Li P, Li Y, Lin Y, Huang B, Sun L, Ma W, Fan X, Wen W, Lei W. Validity study of a multiscaled fusion network using single-lead electrocardiogram signals for obstructive sleep apnea diagnosis. J Clin Sleep Med 2023; 19:1017-1025. [PMID: 36734174 PMCID: PMC10235715 DOI: 10.5664/jcsm.10466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 01/12/2023] [Accepted: 01/12/2023] [Indexed: 02/04/2023]
Abstract
STUDY OBJECTIVES We evaluated the validity of a squeeze-and-excitation and multiscaled fusion network (SE-MSCNN) using single-lead electrocardiogram (ECG) signals for obstructive sleep apnea detection and classification. METHODS Overnight polysomnographic data from 436 participants at the Sleep Center of the First Affiliated Hospital of Sun Yat-sen University were used to generate a new FAH-ECG dataset comprising 260, 88, and 88 single-lead ECG signal recordings for training, validation, and testing, respectively. The SE-MSCNN was employed for detection of apnea-hypopnea events from the acquired ECG segments. Sensitivity, specificity, accuracy, and F1 scores were assigned to assess algorithm performance. We also used the SE-MSCNN to estimate the apnea-hypopnea index, classify obstructive sleep apnea severity, and compare the agreement between 2 sleep technicians. RESULTS The SE-MSCNN's accuracy, sensitivity, specificity, and F1 score on the FAH-ECG dataset were 86.6%, 83.3%, 89.1%, and 0.843, respectively. Although slightly inferior to previously reported results using public datasets, it is superior to state-of-the-art open-source models. Furthermore, the SE-MSCNN had good agreement with manual scoring, such that the Spearman's correlations for the apnea-hypopnea index between the SE-MSCNN and 2 technicians were 0.93 and 0.94, respectively. Cohen's kappa scores in classifying the SE-MSCNN and the 2 sleep technicians were 0.72 and 0.78, respectively. CONCLUSIONS In this study, we validated the use of the SE-MSCNN in a clinical environment, and despite some limitations the network appeared to meet the performance standards for generalizability. Therefore, updating algorithms based on single-lead ECG signals can facilitate the development of novel wearable devices for efficient obstructive sleep apnea screening. CITATION Yue H, Li P, Li Y, et al. Validity study of a multiscaled fusion network using single-lead electrocardiogram signals for obstructive sleep apnea diagnosis. J Clin Sleep Med. 2023;19(6):1017-1025.
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Affiliation(s)
- Huijun Yue
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Pan Li
- School of Computer Science, South China Normal University, Guangzhou, People’s Republic of China
| | - Yun Li
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Yu Lin
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Bixue Huang
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Lin Sun
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Wenjun Ma
- School of Computer Science, South China Normal University, Guangzhou, People’s Republic of China
| | - Xiaomao Fan
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, People’s Republic of China
| | - Weiping Wen
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Wenbin Lei
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of China
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Hemrajani P, Dhaka VS, Rani G, Shukla P, Bavirisetti DP. Efficient Deep Learning Based Hybrid Model to Detect Obstructive Sleep Apnea. SENSORS (BASEL, SWITZERLAND) 2023; 23:4692. [PMID: 37430605 DOI: 10.3390/s23104692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/23/2023] [Accepted: 04/25/2023] [Indexed: 07/12/2023]
Abstract
An increasing number of patients and a lack of awareness about obstructive sleep apnea is a point of concern for the healthcare industry. Polysomnography is recommended by health experts to detect obstructive sleep apnea. The patient is paired up with devices that track patterns and activities during their sleep. Polysomnography, being a complex and expensive process, cannot be adopted by the majority of patients. Therefore, an alternative is required. The researchers devised various machine learning algorithms using single lead signals such as electrocardiogram, oxygen saturation, etc., for the detection of obstructive sleep apnea. These methods have low accuracy, less reliability, and high computation time. Thus, the authors introduced two different paradigms for the detection of obstructive sleep apnea. The first is MobileNet V1, and the other is the convergence of MobileNet V1 with two separate recurrent neural networks, Long-Short Term Memory and Gated Recurrent Unit. They evaluate the efficacy of their proposed method using authentic medical cases from the PhysioNet Apnea-Electrocardiogram database. The model MobileNet V1 achieves an accuracy of 89.5%, a convergence of MobileNet V1 with LSTM achieves an accuracy of 90%, and a convergence of MobileNet V1 with GRU achieves an accuracy of 90.29%. The obtained results prove the supremacy of the proposed approach in comparison to the state-of-the-art methods. To showcase the implementation of devised methods in a real-life scenario, the authors design a wearable device that monitors ECG signals and classifies them into apnea and normal. The device employs a security mechanism to transmit the ECG signals securely over the cloud with the consent of patients.
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Affiliation(s)
- Prashant Hemrajani
- Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, Rajasthan, India
| | - Vijaypal Singh Dhaka
- Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, Rajasthan, India
| | - Geeta Rani
- Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, Rajasthan, India
| | - Praveen Shukla
- Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, Rajasthan, India
| | - Durga Prasad Bavirisetti
- Department of Computer Science, Norwegian University of Science and Technology, 7034 Trondheim, Norway
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Cheng L, Luo S, Li B, Liu R, Zhang Y, Zhang H. Multiple-instance learning for EEG based OSA event detection. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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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: 8] [Impact Index Per Article: 4.0] [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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Jiménez-García J, García M, Gutiérrez-Tobal GC, Kheirandish-Gozal L, Vaquerizo-Villar F, Álvarez D, del Campo F, Gozal D, Hornero R. A 2D convolutional neural network to detect sleep apnea in children using airflow and oximetry. Comput Biol Med 2022; 147:105784. [DOI: 10.1016/j.compbiomed.2022.105784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 04/19/2022] [Accepted: 06/26/2022] [Indexed: 11/03/2022]
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