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Chao YP, Chuang HH, Lee ZH, Huang SY, Zhan WT, Shyu LY, Lo YL, Lee GS, Li HY, Lee LA. Distinguishing severe sleep apnea from habitual snoring using a neck-wearable piezoelectric sensor and deep learning: A pilot study. Comput Biol Med 2025; 190:110070. [PMID: 40147187 DOI: 10.1016/j.compbiomed.2025.110070] [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: 10/12/2024] [Revised: 01/29/2025] [Accepted: 03/21/2025] [Indexed: 03/29/2025]
Abstract
This study explores the development of a deep learning model using a neck-wearable piezoelectric sensor to accurately distinguish severe sleep apnea syndrome (SAS) from habitual snoring, addressing the underdiagnosis of SAS in adults. From 2018 to 2020, 60 adult habitual snorers underwent polysomnography while wearing a neck piezoelectric sensor that recorded snoring vibrations (70-250 Hz) and carotid artery pulsations (0.01-1.5 Hz). The initial dataset comprised 1167 silence, 1304 snoring, and 399 noise samples from 20 participants. Using a hybrid deep learning model comprising a one-dimensional convolutional neural network and gated-recurrent unit, the model identified snoring and apnea/hypopnea events, with sleep phases detected via pulse wave variability criteria. The model's efficacy in predicting severe SAS was assessed in the remaining 40 participants, achieving snoring detection rates of 0.88, 0.86, and 0.92, with respective loss rates of 0.39, 0.90, and 0.23. Classification accuracy for severe SAS improved from 0.85 for total sleep time to 0.90 for partial sleep time, excluding the first sleep phase, demonstrating precision of 0.84, recall of 1.00, and an F1 score of 0.91. This innovative approach of combining a hybrid deep learning model with a neck-wearable piezoelectric sensor suggests a promising route for early and precise differentiation of severe SAS from habitual snoring, aiding guiding further standard diagnostic evaluations and timely patient management. Future studies should focus on expanding the sample size, diversifying the patient population, and external validations in real-world settings to enhance the robustness and applicability of the findings.
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Affiliation(s)
- Yi-Ping Chao
- Department of Computer Science and Information Engineering, Chang Gung University, 33302, Taoyuan, Taiwan; Department of Otorhinolaryngology, Head and Neck Surgery, Sleep Center, Linkou Medical Center, Chang Gung Memorial Hospital, Chang Gung University, 33305 Taoyuan, Taiwan
| | - Hai-Hua Chuang
- Department of Community Medicine, Cathay General Hospital, 10630 Taipei, Taiwan; School of Medicine, College of Life Science and Medicine, National Tsing Hua University, 300044, Hsinchu, Taiwan; Department of Industrial Engineering and Management, National Taipei University of Technology, 10608, Taipei, Taiwan
| | - Zong-Han Lee
- Department of Biomedical Engineering, Chung Yuan Christian University, 320314 Taoyuan, Taiwan
| | - Shu-Yi Huang
- Department of Biomedical Engineering, Chung Yuan Christian University, 320314 Taoyuan, Taiwan
| | - Wan-Ting Zhan
- Department of Biomedical Engineering, Chung Yuan Christian University, 320314 Taoyuan, Taiwan
| | - Liang-Yu Shyu
- Department of Biomedical Engineering, Chung Yuan Christian University, 320314 Taoyuan, Taiwan
| | - Yu-Lun Lo
- Department of Pulmonary and Critical Care Medicine, Linkou Main Branch, Chang Gung Memorial Hospital, Chang Gung University, 33305, Taoyuan, Taiwan
| | - Guo-She Lee
- Faculty of Medicine, National Yang Ming Chiao Tung University, 112304, Taipei, Taiwan; Department of Otolaryngology, Taipei City Hospital, Ren-Ai Branch, 106243, Taipei, Taiwan
| | - Hsueh-Yu Li
- Department of Otorhinolaryngology, Head and Neck Surgery, Sleep Center, Linkou Medical Center, Chang Gung Memorial Hospital, Chang Gung University, 33305 Taoyuan, Taiwan
| | - Li-Ang Lee
- Department of Otorhinolaryngology, Head and Neck Surgery, Sleep Center, Linkou Medical Center, Chang Gung Memorial Hospital, Chang Gung University, 33305 Taoyuan, Taiwan; School of Medicine, College of Life Science and Medicine, National Tsing Hua University, 300044, Hsinchu, Taiwan.
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Linh TTD, Trang NTH, Lin SY, Wu D, Liu WT, Hu CJ. Detection of preceding sleep apnea using ECG spectrogram during CPAP titration night: A novel machine-learning and bag-of-features framework. J Sleep Res 2024; 33:e13991. [PMID: 37402610 DOI: 10.1111/jsr.13991] [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: 02/27/2023] [Revised: 06/18/2023] [Accepted: 06/23/2023] [Indexed: 07/06/2023]
Abstract
Obstructive sleep apnea (OSA) has a heavy health-related burden on patients and the healthcare system. Continuous positive airway pressure (CPAP) is effective in treating OSA, but adherence to it is often inadequate. A promising solution is to detect sleep apnea events in advance, and to adjust the pressure accordingly, which could improve the long-term use of CPAP treatment. The use of CPAP titration data may reflect a similar response of patients to therapy at home. Our study aimed to develop a machine-learning algorithm using retrospective electrocardiogram (ECG) data and CPAP titration to forecast sleep apnea events before they happen. We employed a support vector machine (SVM), k-nearest neighbour (KNN), decision tree (DT), and linear discriminative analysis (LDA) to detect sleep apnea events 30-90 s in advance. Preprocessed 30 s segments were time-frequency transformed to spectrograms using continuous wavelet transform, followed by feature generation using the bag-of-features technique. Specific frequency bands of 0.5-50 Hz, 0.8-10 Hz, and 8-50 Hz were also extracted to detect the most detected band. Our results indicated that SVM outperformed KNN, LDA, and DT across frequency bands and leading time segments. The 8-50 Hz frequency band gave the best accuracy of 98.2%, and a F1-score of 0.93. Segments 60 s before sleep events seemed to exhibit better performance than other pre-OSA segments. Our findings demonstrate the feasibility of detecting sleep apnea events in advance using only a single-lead ECG signal at CPAP titration, making our proposed framework a novel and promising approach to managing obstructive sleep apnea at home.
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Affiliation(s)
- Tran Thanh Duy Linh
- International Ph.D. Program of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Family Medicine Training Center, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Nguyen Thi Hoang Trang
- Department of Biomedical Engineering, College of Engineering, National Cheng Kung University, Tainan, Taiwan
| | - Shang-Yang Lin
- Research Center of Sleep Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Dean Wu
- Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
- Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Sleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Wen-Te Liu
- Sleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
- 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
- Research Center of Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan
| | - Chaur-Jong Hu
- Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
- Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
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Tsai CY, Liu WT, Hsu WH, Majumdar A, Stettler M, Lee KY, Cheng WH, Wu D, Lee HC, Kuan YC, Wu CJ, Lin YC, Ho SC. Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events. Digit Health 2023; 9:20552076231152751. [PMID: 36896329 PMCID: PMC9989412 DOI: 10.1177/20552076231152751] [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: 10/29/2022] [Accepted: 01/04/2023] [Indexed: 03/08/2023] Open
Abstract
Objectives Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features. Methods We collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naïve Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening. Results The RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk. Conclusions The established model can be considered for screening for the risk of having moderate-to-severe or severe OSA.
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Affiliation(s)
- Cheng-Yu Tsai
- Department of Civil and Environmental Engineering, Imperial College London, London, UK
| | - Wen-Te Liu
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Sleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Research Center of Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan
| | - Wen-Hua Hsu
- 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, Taiwan
| | - Wun-Hao Cheng
- Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Dean Wu
- Sleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Department of Neurology, Shuang Ho Hospital, Taipei Medical University, 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.,Dementia Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Hsin-Chien Lee
- Department of Psychiatry, Taipei Medical University Hospital, Taipei, Taiwan
| | - Yi-Chun Kuan
- Sleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Department of Neurology, Shuang Ho Hospital, Taipei Medical University, 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.,Dementia Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Cheng-Jung Wu
- Department of Otolaryngology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Yi-Chih Lin
- Department of Otolaryngology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Shu-Chuan Ho
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
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