Quantification of Resting-State Ballistocardiogram Difference Between Clinical and Non-Clinical Populations for Ambient Monitoring of Heart Failure

IEEE Journal of Translational Engineering in Health and Medicine

Narges Armanfard¹  Susanna Mak²  Jennifer Boger³  Sherry L. Grace⁴  Isaac Sungjae Chang⁵  Caroline Chessex⁶  Amaya Arcelus⁶  Alex Mihailidis⁶ 

[1] Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada;Department of Medicine, Division of Cardiology, Mount Sinai Hospital, Toronto, ON, Canada;Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada;Faculty of Health, York University, Toronto, ON, Canada;Institute of Biomaterials and Biomedical Engineering, University of Toronto, ON, Canada;Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada;

关键词: Ballistocardiogram;    resting-state;    heart failure;    ambient monitoring;   

DOI  :  10.1109/JTEHM.2020.3029690

来源: DOAJ

【 摘 要 】

A ballistocardiogram (BCG) is a versatile bio-signal that enables ambient remote monitoring of heart failure (HF) patients in a home setting, achieved through embedded sensors in the surrounding environment. Numerous methods of analysis are available for extracting physiological information using the BCG; however, most have been developed based on non-clinical subjects. While the difference between clinical and non-clinical populations are expected, quantification of the difference may serve as a useful tool. In this work, the differences in resting-state BCGs of the two cohorts in a sitting posture were quantified. An instrumented chair was used to collect the BCG from 29 healthy adults and 26 NYHA HF class I and II patients while seated without any stress test for five minutes. Five 20-second epochs per subject were used to calculate the waveform fluctuation metric at rest (WFMR). The WFMR was obtained in two steps. The ensemble average of the segmented BCG heartbeats within an epoch were calculated first. Mean square errors (MSE) between different ensemble average pairs were then retrieved. The MSEs were averaged to produce the WFMR. The comparison showed that the clinical cohort had higher fluctuation than the non-clinical population and had at least 82.2% separation, suggesting that greater errors may result when existing algorithms were used. The WFMR acts as a bridge that may enable important features, including the addition of error margins in parameter estimation and ways to devise a calibration strategy when resting-state BCG is unstable.

【 授权许可】

Unknown