Monitoring Respiratory Health in Children With Acute Asthma Using Wearable Electrical Bioimpedance and Breath Sounds: Observational Case-Control Study

TL;DR

This study shows how wearable sensors can track respiratory health in children with acute asthma, offering a new way to monitor and manage the condition in real time.

Contribution

The study introduces a novel approach using wearable multimodal sensing to quantify respiratory health in children with acute asthma.

Findings

Respiratory rate decreased while expiration and inspiration times increased during recovery from acute asthma.

Acoustic features in specific frequency bands changed significantly, showing trends toward normalcy.

All features were significantly different between acute asthma and control groups, supporting the feasibility of the method.

Abstract

Asthma remains one of the most serious chronic diseases of childhood. Individuals with severe asthma experience sudden episodes of breathlessness due to acute airflow obstruction, leading to recurrent pediatric intensive care unit (PICU) admissions that often result in mechanical ventilation and even death. Existing clinical assessments lack temporal resolution to effectively track the rapidly changing physiology.

This study aimed to evaluate the feasibility of quantifying respiratory health during acute asthma in children using wearable multimodal sensing.

Wearable-based impedance pneumography (IP) and multichannel lung sounds (LSs) were measured on 17 children admitted to the PICU with an acute asthma attack and on 9 healthy controls. Short-term multimodal measurements were obtained throughout hospitalization, specifically at PICU admission (T1) and discharge (T2). Measurements were also obtained from controls without any signs of acute asthma or otherwise healthy. Statistical and clustering analyses were performed to identify trends in IP- and LS-derived respiratory markers from T1 to T2 across all patients with paired time points (n=13), as well as across the matched cohort (T1: n=10 and T2: n=7), who were compared against controls (n=9). Five features were computed from the IP signal: respiratory rate, inspiration time (Ti), expiration time (Te), expiration-to-inspiration time ratio (Te:Ti), and the normalized Ti by interbreath interval (Ti/IBI). Leveraging the breathing context provided by the IP signal, 4 spectral integrated intensity (SI) acoustic features were computed in 4 different subbands for the inspiration and expiration phases.

Within the patient group (n=13), we found that respiratory rate decreased (W(12)=79; P=.02), whereas Te (W(12)=12; P=.02) and Ti (W(12)=13; P=.02) lengthened. Meanwhile, the SIs for the lowest subband (100-300 Hz) decreased for both inspiration and expiration phases (P<.01), while they increased for the highest subband (800-1000 Hz) for both inspiration and expiration phases (P<.01). Significant differences also existed between T1 and control and T2 and control of the matched cohort. We found that all features were significantly different between T1 and control (P<.05), and all SIs together with Te:Ti and Ti/IBI were significantly different between T2 and control (P<.05), all exhibiting trends toward normalcy.

These results demonstrate the feasibility of quantifying and tracking respiratory health in children with acute asthma using wearable multimodal sensing, specifically with the fusion of IP- and LS-derived markers. Such technology may provide a new adjunctive clinical tool for real-time respiratory monitoring and enable timely titration of care, thereby improving patient outcomes.

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