Evaluating Cardiac Impairment From Abnormal Respiratory Patterns: Insights From a Wireless Radar and Deep Learning Study
Objectives: Assessing the bidirectional impacts of heart function impairment and sleep-disordered breathing remains underexplored. Thus, this study analyzed respiratory patterns from a wireless radar framework to explore their associations with echocardiographic (2D-echo) measurements. Methods: Back...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Translational Engineering in Health and Medicine |
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| author | Chun-Chih Chiu Wen-Te Liu Jiunn-Horng Kang Chun-Chao Chen Yu-Hsuan Ho Yu-Wen Huang Zong-Lin Tsai Rachel Chien Ying-Ying Chen Yen-Ling Chen Nai-Wen Chang Hung-Wen Lu Kang-Yun Lee Arnab Majumdar Shu-Han Liao Ju-Chi Liu Cheng-Yu Tsai |
| author_facet | Chun-Chih Chiu Wen-Te Liu Jiunn-Horng Kang Chun-Chao Chen Yu-Hsuan Ho Yu-Wen Huang Zong-Lin Tsai Rachel Chien Ying-Ying Chen Yen-Ling Chen Nai-Wen Chang Hung-Wen Lu Kang-Yun Lee Arnab Majumdar Shu-Han Liao Ju-Chi Liu Cheng-Yu Tsai |
| author_sort | Chun-Chih Chiu |
| collection | DOAJ |
| description | Objectives: Assessing the bidirectional impacts of heart function impairment and sleep-disordered breathing remains underexplored. Thus, this study analyzed respiratory patterns from a wireless radar framework to explore their associations with echocardiographic (2D-echo) measurements. Methods: Background details, 2D-echo parameters, and biochemical data were collected from patients in a cardiology ward in northern Taiwan. Their radar-based respiratory patterns from the night before and the night of the 2D-echo were obtained, averaged, and used to derive indices such as the respiratory disturbance index (RDI) and periodic breathing (PB) cycle length, representing overall respiratory patterns. Next, retrieved data were grouped based on a 50% left ventricular ejection fraction (LVEF) threshold and analyzed using mean comparisons and regression models to explore relationships. Results: Patients with an LVEF of <inline-formula> <tex-math notation="LaTeX">$\le 50$ </tex-math></inline-formula>% demonstrated significantly reduced total sleep time, higher RDI, and longer PB cycles compared to those with LVEF >50%. Each 1-event/h increase in the RDI reduced the LVEF by 0.22% (95% confidence interval [CI]: −0.41% to −0.03%, p <0.05), and each 1-s increase in the PB cycle length was associated with a 0.21% LVEF reduction (95% CI: −0.35% to −0.07%). Increases in RDI and PB cycle length were associated with a heightened risk of LVEF declining to <inline-formula> <tex-math notation="LaTeX">$\le 50$ </tex-math></inline-formula>% from >50%. Subgroup analysis revealed that the PB cycle length was associated with elevated N-terminal-prohormone-brain-natriuretic-peptide (NT-proBNP) levels. Conclusions: This study demonstrates that a wireless radar framework combined with deep learning can effectively monitor respiratory patterns that are associated with cardiac function. Its contactless nature may support continuous cardiac function assessments. Clinical Impact: This study highlights the effectiveness of a wireless radar and deep learning framework for monitoring respiratory patterns that are associated with cardiac function (e.g., LVEF), underscoring its potential for long-term cardiac and sleep-disorder management. |
| format | Article |
| id | doaj-art-a5427df5854e4a5eb14dcf95f04f79a7 |
| institution | Matheson Library |
| issn | 2168-2372 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Journal of Translational Engineering in Health and Medicine |
| spelling | doaj-art-a5427df5854e4a5eb14dcf95f04f79a72025-07-24T23:00:34ZengIEEEIEEE Journal of Translational Engineering in Health and Medicine2168-23722025-01-011332333210.1109/JTEHM.2025.358852311079612Evaluating Cardiac Impairment From Abnormal Respiratory Patterns: Insights From a Wireless Radar and Deep Learning StudyChun-Chih Chiu0Wen-Te Liu1Jiunn-Horng Kang2https://orcid.org/0000-0002-7850-4140Chun-Chao Chen3Yu-Hsuan Ho4Yu-Wen Huang5Zong-Lin Tsai6Rachel Chien7Ying-Ying Chen8Yen-Ling Chen9Nai-Wen Chang10Hung-Wen Lu11Kang-Yun Lee12Arnab Majumdar13Shu-Han Liao14https://orcid.org/0000-0001-7758-9843Ju-Chi Liu15Cheng-Yu Tsai16https://orcid.org/0000-0002-1639-4257Department of Cardiology, Taipei Medical University-Shuang Ho Hospital, New Taipei City, TaiwanSchool of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, TaiwanTMU Research Center of Artificial Intelligence in Medicine and Health, Taipei Medical University, Taipei, TaiwanDepartment of Cardiology, Taipei Medical University-Shuang Ho Hospital, New Taipei City, TaiwanWireless Technology and Antenna Research and Development Department, Wistron Corporation, Taipei, TaiwanWireless Technology and Antenna Research and Development Department, Wistron Corporation, Taipei, TaiwanWireless Technology and Antenna Research and Development Department, Wistron Corporation, Taipei, TaiwanResearch Center of Sleep Medicine, College of Medicine, Taipei Medical University, Taipei, TaiwanResearch Center of Sleep Medicine, College of Medicine, Taipei Medical University, Taipei, TaiwanGraduate Institute of Nanomedicine and Medical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei, TaiwanResearch Center of Sleep Medicine, College of Medicine, Taipei Medical University, Taipei, TaiwanResearch Center of Sleep Medicine, College of Medicine, Taipei Medical University, Taipei, TaiwanDepartment of Internal Medicine, Division of Pulmonary Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City, TaiwanDepartment of Civil and Environmental Engineering, Imperial College London, London, U.K.Department of Electrical and Computer Engineering, Tamkang University, Tamsui, New Taipei City, TaiwanDepartment of Cardiology, Taipei Medical University-Shuang Ho Hospital, New Taipei City, TaiwanSchool of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, TaiwanObjectives: Assessing the bidirectional impacts of heart function impairment and sleep-disordered breathing remains underexplored. Thus, this study analyzed respiratory patterns from a wireless radar framework to explore their associations with echocardiographic (2D-echo) measurements. Methods: Background details, 2D-echo parameters, and biochemical data were collected from patients in a cardiology ward in northern Taiwan. Their radar-based respiratory patterns from the night before and the night of the 2D-echo were obtained, averaged, and used to derive indices such as the respiratory disturbance index (RDI) and periodic breathing (PB) cycle length, representing overall respiratory patterns. Next, retrieved data were grouped based on a 50% left ventricular ejection fraction (LVEF) threshold and analyzed using mean comparisons and regression models to explore relationships. Results: Patients with an LVEF of <inline-formula> <tex-math notation="LaTeX">$\le 50$ </tex-math></inline-formula>% demonstrated significantly reduced total sleep time, higher RDI, and longer PB cycles compared to those with LVEF >50%. Each 1-event/h increase in the RDI reduced the LVEF by 0.22% (95% confidence interval [CI]: −0.41% to −0.03%, p <0.05), and each 1-s increase in the PB cycle length was associated with a 0.21% LVEF reduction (95% CI: −0.35% to −0.07%). Increases in RDI and PB cycle length were associated with a heightened risk of LVEF declining to <inline-formula> <tex-math notation="LaTeX">$\le 50$ </tex-math></inline-formula>% from >50%. Subgroup analysis revealed that the PB cycle length was associated with elevated N-terminal-prohormone-brain-natriuretic-peptide (NT-proBNP) levels. Conclusions: This study demonstrates that a wireless radar framework combined with deep learning can effectively monitor respiratory patterns that are associated with cardiac function. Its contactless nature may support continuous cardiac function assessments. Clinical Impact: This study highlights the effectiveness of a wireless radar and deep learning framework for monitoring respiratory patterns that are associated with cardiac function (e.g., LVEF), underscoring its potential for long-term cardiac and sleep-disorder management.https://ieeexplore.ieee.org/document/11079612/Sleep-disordered breathingechocardiographic (2D-echo) measurementsrespiratory disturbance index (RDI)periodic breathing (PB) cycle lengthleft ventricular ejection fraction (LVEF) |
| spellingShingle | Chun-Chih Chiu Wen-Te Liu Jiunn-Horng Kang Chun-Chao Chen Yu-Hsuan Ho Yu-Wen Huang Zong-Lin Tsai Rachel Chien Ying-Ying Chen Yen-Ling Chen Nai-Wen Chang Hung-Wen Lu Kang-Yun Lee Arnab Majumdar Shu-Han Liao Ju-Chi Liu Cheng-Yu Tsai Evaluating Cardiac Impairment From Abnormal Respiratory Patterns: Insights From a Wireless Radar and Deep Learning Study IEEE Journal of Translational Engineering in Health and Medicine Sleep-disordered breathing echocardiographic (2D-echo) measurements respiratory disturbance index (RDI) periodic breathing (PB) cycle length left ventricular ejection fraction (LVEF) |
| title | Evaluating Cardiac Impairment From Abnormal Respiratory Patterns: Insights From a Wireless Radar and Deep Learning Study |
| title_full | Evaluating Cardiac Impairment From Abnormal Respiratory Patterns: Insights From a Wireless Radar and Deep Learning Study |
| title_fullStr | Evaluating Cardiac Impairment From Abnormal Respiratory Patterns: Insights From a Wireless Radar and Deep Learning Study |
| title_full_unstemmed | Evaluating Cardiac Impairment From Abnormal Respiratory Patterns: Insights From a Wireless Radar and Deep Learning Study |
| title_short | Evaluating Cardiac Impairment From Abnormal Respiratory Patterns: Insights From a Wireless Radar and Deep Learning Study |
| title_sort | evaluating cardiac impairment from abnormal respiratory patterns insights from a wireless radar and deep learning study |
| topic | Sleep-disordered breathing echocardiographic (2D-echo) measurements respiratory disturbance index (RDI) periodic breathing (PB) cycle length left ventricular ejection fraction (LVEF) |
| url | https://ieeexplore.ieee.org/document/11079612/ |
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