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Updated: Sep 10, 2025

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Wearable Device-Based Respiratory Complexity Analysis for Detecting Pulmonary Congestion in Patients With Heart

Mengwei Li1,2, Yu Kang3, Shuanglin Zhao4

  • 1Department of Medical Engineering, Beidaihe Rest and Recuperation Center of PLA, Qinhuangdao, China.

Journal of Medical Internet Research
|August 27, 2025
PubMed
Summary

Wearable devices can detect respiratory changes in heart failure (HF) patients with pulmonary congestion (PC). This technology shows promise for early intervention and reducing hospitalizations by identifying specific breathing patterns associated with PC.

Keywords:
heart failuremultiscale entropypulmonary congestionrespiratory complexitywearable device

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Area of Science:

  • Cardiology and Respiratory Medicine
  • Biomedical Engineering and Wearable Technology
  • Digital Health and Predictive Diagnostics

Background:

  • Excessive pulmonary congestion (PC) significantly contributes to heart failure (HF) deterioration, frequently leading to emergency hospitalizations.
  • Early detection of PC-related respiratory abnormalities using wearable devices could facilitate prompt intervention and decrease hospital admissions.
  • The feasibility of wearables in detecting specific respiratory biomarkers for PC requires further investigation.

Purpose of the Study:

  • To evaluate the feasibility of using wearable devices for monitoring respiratory data in hospitalized HF patients.
  • To determine if wearable-derived respiratory signals can differentiate between HF patients with and without PC.

Main Methods:

  • An observational, exploratory study involving 62 hospitalized HF patients (44 with PC), excluding those with severe lung disease or requiring intensive care.
  • Participants wore a device for ≥24 hours post-admission, with nighttime breathing patterns analyzed for respiratory cycle, amplitude, and multiscale entropy (MSE).
  • Comprehensive clinical evaluations, including lung ultrasound to define PC, were performed within 24 hours.

Main Results:

  • Patients with PC exhibited significantly prolonged mean expiratory time (TE) and elevated expiratory time ratio (TE_ratio) compared to those without PC.
  • Increased multiscale entropy (MSE) values in respiratory amplitude (RA) for scales 1-5 (area_1_5) and 6-20 (area_6_20) were observed in the PC group.
  • Logistic regression identified TE_ratio, RA area_1_5, and RA area_6_20 as significant predictors of PC; a multivariate model achieved an AUC of 0.91.

Conclusions:

  • Wearable-based MSE analysis effectively distinguished hospitalized HF patients with and without PC.
  • Prolonged expiratory phases and increased respiratory amplitude complexity were characteristic of the PC group.
  • These findings suggest the potential of wearables for non-invasive monitoring and early detection of pulmonary congestion in HF.