A Signal Segmentation‐Free Model for Electrocardiogram‐Based Obstructive Sleep Apnea Severity Classification
Obstructive sleep apnea (OSA) has been a common sleep disorder for years, and polysomnography (PSG) remains the gold standard for diagnosing OSA. Nevertheless, PSG is a time and money consuming test, and patients have to wait long for arranging a PSG test in a hospital. In light of this, portable an...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-03-01
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| Series: | Advanced Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aisy.202200275 |
| _version_ | 1797860226254241792 |
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| author | Jeng-Wen Chen Shih-Tsang Lin Cheng-Yi Wang Chun-Cheng Lin Kuan-Chun Hsu Cheng-Yu Yeh Shaw-Hwa Hwang |
| author_facet | Jeng-Wen Chen Shih-Tsang Lin Cheng-Yi Wang Chun-Cheng Lin Kuan-Chun Hsu Cheng-Yu Yeh Shaw-Hwa Hwang |
| author_sort | Jeng-Wen Chen |
| collection | DOAJ |
| description | Obstructive sleep apnea (OSA) has been a common sleep disorder for years, and polysomnography (PSG) remains the gold standard for diagnosing OSA. Nevertheless, PSG is a time and money consuming test, and patients have to wait long for arranging a PSG test in a hospital. In light of this, portable and wearable tools for OSA classification have been developed recently as a low‐cost and easy‐to‐use screening method before undergoing PSG. Using unsegmented electrocardiogram (ECG) signals, a deep neural network (DNN)‐based model is developed here to categorize OSA severity with the following features. First, the model takes unsegmented ECG signals recorded overnight as input, and then generates a four‐level scale as output. Since all the input ECG signals are unsegmented, the tremendous amount of effort spent on signal annotation can be fully saved. Second, the largest amount of data is used to test the model and consequently provide a high generalization ability, as compared with others in the literature. The overall outperformance of this work is highlighted at the end of this article, and this work is validated as an easy‐to‐use and effective screening tool for OSA accordingly. |
| first_indexed | 2024-04-09T21:43:35Z |
| format | Article |
| id | doaj.art-8a04b03107c54516833b547131c35d6f |
| institution | Directory Open Access Journal |
| issn | 2640-4567 |
| language | English |
| last_indexed | 2024-04-09T21:43:35Z |
| publishDate | 2023-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj.art-8a04b03107c54516833b547131c35d6f2023-03-25T19:40:27ZengWileyAdvanced Intelligent Systems2640-45672023-03-0153n/an/a10.1002/aisy.202200275A Signal Segmentation‐Free Model for Electrocardiogram‐Based Obstructive Sleep Apnea Severity ClassificationJeng-Wen Chen0Shih-Tsang Lin1Cheng-Yi Wang2Chun-Cheng Lin3Kuan-Chun Hsu4Cheng-Yu Yeh5Shaw-Hwa Hwang6Department of Otolaryngology-Head and Neck Surgery Cardinal Tien Hospital and School of Medicine College of Medicine Fu Jen Catholic University 362, Zhongzheng Rd., Xindian Dist. New Taipei City 23148 TaiwanDepartment of Otolaryngology-Head and Neck Surgery Cardinal Tien Hospital and School of Medicine College of Medicine Fu Jen Catholic University 362, Zhongzheng Rd., Xindian Dist. New Taipei City 23148 TaiwanDepartment of Internal Medicine Cardinal Tien Hospital and School of Medicine College of Medicine Fu Jen Catholic University 362, Zhongzheng Rd., Xindian Dist. New Taipei City 23148 TaiwanDepartment of Electrical Engineering National Chin-Yi University of Technology 57, Sec. 2, Zhongshan Rd., Taiping Dist. Taichung 41170 TaiwanDepartment of Electrical Engineering National Chin-Yi University of Technology 57, Sec. 2, Zhongshan Rd., Taiping Dist. Taichung 41170 TaiwanDepartment of Electrical Engineering National Chin-Yi University of Technology 57, Sec. 2, Zhongshan Rd., Taiping Dist. Taichung 41170 TaiwanDepartment of Electronics and Electrical Engineering National Yang Ming Chiao Tung University 1001, Daxue Rd. East Dist. Hsinchu 300093 TaiwanObstructive sleep apnea (OSA) has been a common sleep disorder for years, and polysomnography (PSG) remains the gold standard for diagnosing OSA. Nevertheless, PSG is a time and money consuming test, and patients have to wait long for arranging a PSG test in a hospital. In light of this, portable and wearable tools for OSA classification have been developed recently as a low‐cost and easy‐to‐use screening method before undergoing PSG. Using unsegmented electrocardiogram (ECG) signals, a deep neural network (DNN)‐based model is developed here to categorize OSA severity with the following features. First, the model takes unsegmented ECG signals recorded overnight as input, and then generates a four‐level scale as output. Since all the input ECG signals are unsegmented, the tremendous amount of effort spent on signal annotation can be fully saved. Second, the largest amount of data is used to test the model and consequently provide a high generalization ability, as compared with others in the literature. The overall outperformance of this work is highlighted at the end of this article, and this work is validated as an easy‐to‐use and effective screening tool for OSA accordingly.https://doi.org/10.1002/aisy.202200275apnea-hypopnea index (AHI)deep learningdeep neural network (DNN)electrocardiogram (ECG)obstructive sleep apnea (OSA) |
| spellingShingle | Jeng-Wen Chen Shih-Tsang Lin Cheng-Yi Wang Chun-Cheng Lin Kuan-Chun Hsu Cheng-Yu Yeh Shaw-Hwa Hwang A Signal Segmentation‐Free Model for Electrocardiogram‐Based Obstructive Sleep Apnea Severity Classification Advanced Intelligent Systems apnea-hypopnea index (AHI) deep learning deep neural network (DNN) electrocardiogram (ECG) obstructive sleep apnea (OSA) |
| title | A Signal Segmentation‐Free Model for Electrocardiogram‐Based Obstructive Sleep Apnea Severity Classification |
| title_full | A Signal Segmentation‐Free Model for Electrocardiogram‐Based Obstructive Sleep Apnea Severity Classification |
| title_fullStr | A Signal Segmentation‐Free Model for Electrocardiogram‐Based Obstructive Sleep Apnea Severity Classification |
| title_full_unstemmed | A Signal Segmentation‐Free Model for Electrocardiogram‐Based Obstructive Sleep Apnea Severity Classification |
| title_short | A Signal Segmentation‐Free Model for Electrocardiogram‐Based Obstructive Sleep Apnea Severity Classification |
| title_sort | signal segmentation free model for electrocardiogram based obstructive sleep apnea severity classification |
| topic | apnea-hypopnea index (AHI) deep learning deep neural network (DNN) electrocardiogram (ECG) obstructive sleep apnea (OSA) |
| url | https://doi.org/10.1002/aisy.202200275 |
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