Utilizing a Wireless Radar Framework in Combination With Deep Learning Approaches to Evaluate Obstructive Sleep Apnea Severity in Home-Setting Environments
Kun-Ta Lee,1 Wen-Te Liu,2– 6 Yi-Chih Lin,3,7 Zhihe Chen,8 Yu-Hsuan Ho,9 Yu-Wen Huang,9 Zong-Lin Tsai,9 Chih-Wei Hsu,9 Shang-Min Yeh,9 Hsiao Yi Lin,9 Arnab Majumdar,8 Yen-Ling Chen,10 Yi-Chun Kuan,3,11– 13 Kang-Yun Lee,4,6 Po-Hao Feng,4,6 Kuan-Yuan Chen,4,6 Jiunn-Horng Kang,5,6,14– 16 Hsin-Chien Lee,...
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Dove Medical Press
2025-01-01
|
| Series: | Journal of Multidisciplinary Healthcare |
| Subjects: | |
| Online Access: | https://www.dovepress.com/utilizing-a-wireless-radar-framework-in-combination-with-deep-learning-peer-reviewed-fulltext-article-JMDH |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832590413769211904 |
|---|---|
| author | Lee KT Liu WT Lin YC Chen Z Ho YH Huang YW Tsai ZL Hsu CW Yeh SM Lin HY Majumdar A Chen YL Kuan YC Lee KY Feng PH Chen KY Kang JH Lee HC Ho SC Tsai CY |
| author_facet | Lee KT Liu WT Lin YC Chen Z Ho YH Huang YW Tsai ZL Hsu CW Yeh SM Lin HY Majumdar A Chen YL Kuan YC Lee KY Feng PH Chen KY Kang JH Lee HC Ho SC Tsai CY |
| author_sort | Lee KT |
| collection | DOAJ |
| description | Kun-Ta Lee,1 Wen-Te Liu,2– 6 Yi-Chih Lin,3,7 Zhihe Chen,8 Yu-Hsuan Ho,9 Yu-Wen Huang,9 Zong-Lin Tsai,9 Chih-Wei Hsu,9 Shang-Min Yeh,9 Hsiao Yi Lin,9 Arnab Majumdar,8 Yen-Ling Chen,10 Yi-Chun Kuan,3,11– 13 Kang-Yun Lee,4,6 Po-Hao Feng,4,6 Kuan-Yuan Chen,4,6 Jiunn-Horng Kang,5,6,14– 16 Hsin-Chien Lee,17 Shu-Chuan Ho,2,6,* Cheng-Yu Tsai2– 6,16,18,* 1Respiratory Therapy Room, Division of Pulmonary Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan; 2School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan; 3Sleep Center, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan; 4Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan; 5Research Center of Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan; 6Research Center of Thoracic Medicine, Taipei Medical University, Taipei, Taiwan; 7Department of Otolaryngology, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan; 8Department of Civil and Environmental Engineering, Imperial College London, London, UK; 9Advanced Technology Lab, Wistron Corporation, Taipei, Taiwan; 10Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan; 11Department of Neurology, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan; 12Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; 13Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan; 14Department of Physical Medicine and Rehabilitation, Taipei Medical University Hospital, Taipei, Taiwan; 15Graduate Institute of Nanomedicine and Medical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei, Taiwan; 16Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; 17Department of Psychiatry, Taipei Medical University Hospital, Taipei, Taiwan; 18School of Biomedical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei, Taiwan*These authors contributed equally to this workCorrespondence: Cheng-Yu Tsai, School of Biomedical Engineering, College of Biomedical Engineering, Taipei Medical University, 250 Wuxing Street, Taipei, 11031, Taiwan, Tel +886-2-2736-1661, Fax +886-2-2739-1143, Email b117101018@tmu.edu.tw Shu-Chuan Ho, School of Respiratory Therapy, College of Medicine, Taipei Medical University, 250 Wuxing Street, Taipei, 11031, Taiwan, Tel +886-2-2736-1661, Fax +886-2-2739-1143, Email shu-chuan@tmu.edu.twObjective: Common examinations for diagnosing obstructive sleep apnea (OSA) are polysomnography (PSG) and home sleep apnea testing (HSAT). However, both PSG and HSAT require that sensors be attached to a subject, which may disturb their sleep and affect the results. Hence, in this study, we aimed to verify a wireless radar framework combined with deep learning techniques to screen for the risk of OSA in home-based environments.Methods: This study prospectively collected home-based sleep parameters from 80 participants over 147 nights using both HSAT and a 24-GHz wireless radar framework. The proposed framework, using hybrid models (ie, deep neural decision trees), identified respiratory events by analyzing continuous-wave signals indicative of breathing patterns. Analyses were performed to examine correlations and agreement of the apnea-hypopnea index (AHI) with results obtained through HSAT and the radar-based respiratory disturbance index based on the time in bed from HSAT (bRDITIB). Additionally, Youden’s index was used to establish cutoff thresholds for the bRDITIB, followed by multiclass classification and outcome comparisons.Results: A strong correlation (ρ = 0.87) and high agreement (93.88% within the 95% confidence interval; 138/147) between the AHI and bRDITIB were identified. The moderate-to-severe OSA model achieved 83.67% accuracy (with a bRDITIB cutoff of 21.19 events/h), and the severe OSA model demonstrated 93.21% accuracy (with a bRDITIB cutoff of 28.14 events/h). The average accuracy of multiclass classification using these thresholds was 78.23%.Conclusion: The proposed framework, with its cutoff thresholds, has the potential to be applied in home settings as a surrogate for HSAT, offering acceptable accuracy in screening for OSA without the interference of attached sensors. However, further optimization and verification of the radar-based total sleep time function are necessary for independent application.Keywords: obstructive sleep apnea, OSA, home sleep apnea testing, HSAT, wireless radar framework, apnea-hypopnea index, AHI, respiratory disturbance index based on the time in bed from HSAT, bRDITIB |
| format | Article |
| id | doaj-art-a83e801940fc4ec1b379b6d39e102f33 |
| institution | Kabale University |
| issn | 1178-2390 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | Journal of Multidisciplinary Healthcare |
| spelling | doaj-art-a83e801940fc4ec1b379b6d39e102f332025-01-23T18:50:33ZengDove Medical PressJournal of Multidisciplinary Healthcare1178-23902025-01-01Volume 1838139399522Utilizing a Wireless Radar Framework in Combination With Deep Learning Approaches to Evaluate Obstructive Sleep Apnea Severity in Home-Setting EnvironmentsLee KTLiu WTLin YCChen ZHo YHHuang YWTsai ZLHsu CWYeh SMLin HYMajumdar AChen YLKuan YCLee KYFeng PHChen KYKang JHLee HCHo SCTsai CYKun-Ta Lee,1 Wen-Te Liu,2– 6 Yi-Chih Lin,3,7 Zhihe Chen,8 Yu-Hsuan Ho,9 Yu-Wen Huang,9 Zong-Lin Tsai,9 Chih-Wei Hsu,9 Shang-Min Yeh,9 Hsiao Yi Lin,9 Arnab Majumdar,8 Yen-Ling Chen,10 Yi-Chun Kuan,3,11– 13 Kang-Yun Lee,4,6 Po-Hao Feng,4,6 Kuan-Yuan Chen,4,6 Jiunn-Horng Kang,5,6,14– 16 Hsin-Chien Lee,17 Shu-Chuan Ho,2,6,* Cheng-Yu Tsai2– 6,16,18,* 1Respiratory Therapy Room, Division of Pulmonary Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan; 2School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan; 3Sleep Center, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan; 4Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan; 5Research Center of Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan; 6Research Center of Thoracic Medicine, Taipei Medical University, Taipei, Taiwan; 7Department of Otolaryngology, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan; 8Department of Civil and Environmental Engineering, Imperial College London, London, UK; 9Advanced Technology Lab, Wistron Corporation, Taipei, Taiwan; 10Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan; 11Department of Neurology, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan; 12Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; 13Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan; 14Department of Physical Medicine and Rehabilitation, Taipei Medical University Hospital, Taipei, Taiwan; 15Graduate Institute of Nanomedicine and Medical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei, Taiwan; 16Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; 17Department of Psychiatry, Taipei Medical University Hospital, Taipei, Taiwan; 18School of Biomedical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei, Taiwan*These authors contributed equally to this workCorrespondence: Cheng-Yu Tsai, School of Biomedical Engineering, College of Biomedical Engineering, Taipei Medical University, 250 Wuxing Street, Taipei, 11031, Taiwan, Tel +886-2-2736-1661, Fax +886-2-2739-1143, Email b117101018@tmu.edu.tw Shu-Chuan Ho, School of Respiratory Therapy, College of Medicine, Taipei Medical University, 250 Wuxing Street, Taipei, 11031, Taiwan, Tel +886-2-2736-1661, Fax +886-2-2739-1143, Email shu-chuan@tmu.edu.twObjective: Common examinations for diagnosing obstructive sleep apnea (OSA) are polysomnography (PSG) and home sleep apnea testing (HSAT). However, both PSG and HSAT require that sensors be attached to a subject, which may disturb their sleep and affect the results. Hence, in this study, we aimed to verify a wireless radar framework combined with deep learning techniques to screen for the risk of OSA in home-based environments.Methods: This study prospectively collected home-based sleep parameters from 80 participants over 147 nights using both HSAT and a 24-GHz wireless radar framework. The proposed framework, using hybrid models (ie, deep neural decision trees), identified respiratory events by analyzing continuous-wave signals indicative of breathing patterns. Analyses were performed to examine correlations and agreement of the apnea-hypopnea index (AHI) with results obtained through HSAT and the radar-based respiratory disturbance index based on the time in bed from HSAT (bRDITIB). Additionally, Youden’s index was used to establish cutoff thresholds for the bRDITIB, followed by multiclass classification and outcome comparisons.Results: A strong correlation (ρ = 0.87) and high agreement (93.88% within the 95% confidence interval; 138/147) between the AHI and bRDITIB were identified. The moderate-to-severe OSA model achieved 83.67% accuracy (with a bRDITIB cutoff of 21.19 events/h), and the severe OSA model demonstrated 93.21% accuracy (with a bRDITIB cutoff of 28.14 events/h). The average accuracy of multiclass classification using these thresholds was 78.23%.Conclusion: The proposed framework, with its cutoff thresholds, has the potential to be applied in home settings as a surrogate for HSAT, offering acceptable accuracy in screening for OSA without the interference of attached sensors. However, further optimization and verification of the radar-based total sleep time function are necessary for independent application.Keywords: obstructive sleep apnea, OSA, home sleep apnea testing, HSAT, wireless radar framework, apnea-hypopnea index, AHI, respiratory disturbance index based on the time in bed from HSAT, bRDITIBhttps://www.dovepress.com/utilizing-a-wireless-radar-framework-in-combination-with-deep-learning-peer-reviewed-fulltext-article-JMDHobstructive sleep apneaosahome sleep apnea testinghsatwireless radar frameworkapnea-hypopnea indexahirespiratory disturbance index based on the time in bed from hsatbrditib |
| spellingShingle | Lee KT Liu WT Lin YC Chen Z Ho YH Huang YW Tsai ZL Hsu CW Yeh SM Lin HY Majumdar A Chen YL Kuan YC Lee KY Feng PH Chen KY Kang JH Lee HC Ho SC Tsai CY Utilizing a Wireless Radar Framework in Combination With Deep Learning Approaches to Evaluate Obstructive Sleep Apnea Severity in Home-Setting Environments Journal of Multidisciplinary Healthcare obstructive sleep apnea osa home sleep apnea testing hsat wireless radar framework apnea-hypopnea index ahi respiratory disturbance index based on the time in bed from hsat brditib |
| title | Utilizing a Wireless Radar Framework in Combination With Deep Learning Approaches to Evaluate Obstructive Sleep Apnea Severity in Home-Setting Environments |
| title_full | Utilizing a Wireless Radar Framework in Combination With Deep Learning Approaches to Evaluate Obstructive Sleep Apnea Severity in Home-Setting Environments |
| title_fullStr | Utilizing a Wireless Radar Framework in Combination With Deep Learning Approaches to Evaluate Obstructive Sleep Apnea Severity in Home-Setting Environments |
| title_full_unstemmed | Utilizing a Wireless Radar Framework in Combination With Deep Learning Approaches to Evaluate Obstructive Sleep Apnea Severity in Home-Setting Environments |
| title_short | Utilizing a Wireless Radar Framework in Combination With Deep Learning Approaches to Evaluate Obstructive Sleep Apnea Severity in Home-Setting Environments |
| title_sort | utilizing a wireless radar framework in combination with deep learning approaches to evaluate obstructive sleep apnea severity in home setting environments |
| topic | obstructive sleep apnea osa home sleep apnea testing hsat wireless radar framework apnea-hypopnea index ahi respiratory disturbance index based on the time in bed from hsat brditib |
| url | https://www.dovepress.com/utilizing-a-wireless-radar-framework-in-combination-with-deep-learning-peer-reviewed-fulltext-article-JMDH |
| work_keys_str_mv | AT leekt utilizingawirelessradarframeworkincombinationwithdeeplearningapproachestoevaluateobstructivesleepapneaseverityinhomesettingenvironments AT liuwt utilizingawirelessradarframeworkincombinationwithdeeplearningapproachestoevaluateobstructivesleepapneaseverityinhomesettingenvironments AT linyc utilizingawirelessradarframeworkincombinationwithdeeplearningapproachestoevaluateobstructivesleepapneaseverityinhomesettingenvironments AT chenz utilizingawirelessradarframeworkincombinationwithdeeplearningapproachestoevaluateobstructivesleepapneaseverityinhomesettingenvironments AT hoyh utilizingawirelessradarframeworkincombinationwithdeeplearningapproachestoevaluateobstructivesleepapneaseverityinhomesettingenvironments AT huangyw utilizingawirelessradarframeworkincombinationwithdeeplearningapproachestoevaluateobstructivesleepapneaseverityinhomesettingenvironments AT tsaizl utilizingawirelessradarframeworkincombinationwithdeeplearningapproachestoevaluateobstructivesleepapneaseverityinhomesettingenvironments AT hsucw utilizingawirelessradarframeworkincombinationwithdeeplearningapproachestoevaluateobstructivesleepapneaseverityinhomesettingenvironments AT yehsm utilizingawirelessradarframeworkincombinationwithdeeplearningapproachestoevaluateobstructivesleepapneaseverityinhomesettingenvironments AT linhy utilizingawirelessradarframeworkincombinationwithdeeplearningapproachestoevaluateobstructivesleepapneaseverityinhomesettingenvironments AT majumdara utilizingawirelessradarframeworkincombinationwithdeeplearningapproachestoevaluateobstructivesleepapneaseverityinhomesettingenvironments AT chenyl utilizingawirelessradarframeworkincombinationwithdeeplearningapproachestoevaluateobstructivesleepapneaseverityinhomesettingenvironments AT kuanyc utilizingawirelessradarframeworkincombinationwithdeeplearningapproachestoevaluateobstructivesleepapneaseverityinhomesettingenvironments AT leeky utilizingawirelessradarframeworkincombinationwithdeeplearningapproachestoevaluateobstructivesleepapneaseverityinhomesettingenvironments AT fengph utilizingawirelessradarframeworkincombinationwithdeeplearningapproachestoevaluateobstructivesleepapneaseverityinhomesettingenvironments AT chenky utilizingawirelessradarframeworkincombinationwithdeeplearningapproachestoevaluateobstructivesleepapneaseverityinhomesettingenvironments AT kangjh utilizingawirelessradarframeworkincombinationwithdeeplearningapproachestoevaluateobstructivesleepapneaseverityinhomesettingenvironments AT leehc utilizingawirelessradarframeworkincombinationwithdeeplearningapproachestoevaluateobstructivesleepapneaseverityinhomesettingenvironments AT hosc utilizingawirelessradarframeworkincombinationwithdeeplearningapproachestoevaluateobstructivesleepapneaseverityinhomesettingenvironments AT tsaicy utilizingawirelessradarframeworkincombinationwithdeeplearningapproachestoevaluateobstructivesleepapneaseverityinhomesettingenvironments |