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

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 [Formula: see text]% 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 [Formula: see text]% 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.