期刊論文
| 學年 | 113 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2025-07-14 |
| 作品名稱 | Evaluating Cardiac Impairment from Abnormal Respiratory Patterns: Insights from a Wireless Radar and Deep Learning Study |
| 作品名稱（其他語言） | |
| 著者 | 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 |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | IEEE Journal of Translational Engineering in Health and Medicine 13, p.323-332 |
| 摘要 | 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 ≤50 % 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 ≤50 % 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. |
| 關鍵字 | Sleep-disordered breathing, echocardiographic (2D-echo) measurements, respiratory disturbance index (RDI), periodic breathing (PB) cycle length, left ventricular ejection fraction (LVEF) |
| 語言 | en |
| ISSN | 2168-2372 |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 是 |
| 國別 | USA |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/129514 ) |