Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort signals

Abstract

Purpose: Under the influence of COVID-19 and the in-hospital cost, the in-home detection of cardiovascular disease with smart sensing devices is becoming more popular recently. In the presence of the qualified signals, ballistocardiography (BCG) can not only reflect the cardiac mechanical movements, but also detect the HF in a non-contact manner. However, for the potential HF patients, the additional quality assessment with ECG-aided requires more procedures and brings the inconvenience to their in-home HF diagnosis. To enable the HF detection in many real applications, we proposed a machine learning-aided scheme for the HF detection in this paper, where the BCG signals recorded from the force sensor were employed without the heartbeat location, and the respiratory effort signals separated from force sensors provided more HF features due to the connection between the heart and the lung systems. Finally, the effectiveness of the proposed HF detection scheme was verified in comparative experiments.Methods: First, a piezoelectric sensor was used to record a signal sequences of the two-dimensional vital sign, which includes the BCG and the respiratory effort. Then, the linear and the non-linear features w.r.t. BCG and respiratory effort signals were extracted to serve the HF detection. Finally, the improved HF detection performance was verified through the LOO and the LOSO cross-validation settings with different machine learning classifiers.Results: The proposed machine learning-aided scheme achieved the robust performance in the HF detection by using 4 different classifiers, and yielded an accuracy of 94.97% and 87.00% in the LOO and the LOSO experiments, respectively. In addition, experimental results demonstrated that the designed respiratory and cardiopulmonary features are beneficial to the HF detection (LVEF ≤49%).Conclusion: This study proposed a machine learning-aided HF diagnostic scheme. Experimental results demonstrated that the proposed scheme can fully exploit the relationship between the heart and the lung systems to potentially improve the in-home HF detection performance by using both the BCG, the respiratory and the cardiopulmonary-related features.

Evaluation of a Novel Non-invasive Algorithm to Assess Systolic and Diastolic Function in Normal Subjects

“…[26-28] BCG and SCG have been used together and with machine learning to estimate cardiac kinetic energy and differentiate patients with HF from controls. [29,30] Multiple systems using BCG or SCG to estimate cardiac functional parameters have applied for or have been granted US patents. [31,32] Relationship between Hirsh Algorithm Metrics and Echocardiographic Evaluation of cardiac function with echocardiographic strain and strain rate has provided important insights into systolic and diastolic function across a variety of disease states.…”

Non-invasive technologies for heart failure, systolic and diastolic dysfunction modeling: a scoping review

“…Feng et al used a non-contact piezoelectric sensing device and the echocardiography to detect HF patients with LVEF ≤49 ( Feng et al, 2023 ). Through the extracted linear and nonlinear BCG features, cardiopulmonary, and respiratory features, they demonstrated the contribution between the respiratory and the cardiac systems performance in healthy and HF populations.…”