A comparison of three heart rate detection algorithms over ballistocardiogram signals

TL;DR

This study compares three heart rate detection algorithms applied to diverse ballistocardiogram datasets, demonstrating that continuous wavelet transform with derivative of Gaussian performs best, with all methods capable of real-time analysis on a Raspberry Pi.

Contribution

The paper evaluates and compares three HR detection algorithms across multiple BCG datasets, highlighting the superior performance and efficiency of CWT with derivative of Gaussian.

Findings

CWT with derivative of Gaussian outperforms other algorithms in accuracy.

All methods can analyze 30-second signals in under one second on Raspberry Pi.

MODWT-MRA offers the highest computational performance.

Abstract

Heart rate (HR) detection from ballistocardiogram (BCG) signals is challenging because the signal morphology can vary between and within-subjects. Also, it differs from one sensor to another. Hence, it is essential to evaluate HR detection algorithms across several datasets and under different experimental setups. In this paper, we studied the potential of three HR detection algorithms across four independent BCG datasets. The three algorithms are as follows: the multiresolution analysis of the maximal overlap discrete wavelet transform (MODWT-MRA), continuous wavelet transform (CWT), and template matching (TM). The four datasets were obtained using a microbend fiber optic sensor, a fiber Bragg grating sensor, electromechanical films, and load cells, respectively. The datasets were gathered from: a) 10 patients during a polysomnography study, b) 50 subjects in a sitting position, c) 10 subjects in a sleeping position, and d) 40 subjects in a sleeping position. Overall, CWT with derivative of Gaussian provided superior results compared with the MODWT-MRA, CWT (frequency B-spline), and CWT (Shannon). That said, a BCG template was constructed from DataSet1. Then, it was used for HR detection in the other datasets. The TM method achieved satisfactory results for DataSet2 and DataSet3, but it did not detect the HR of two subjects in DataSet4. The proposed methods were implemented on a Raspberry Pi. As a result, the average time required to analyze a 30-second BCG signal was less than one second for all methods. Yet, the MODWT-MRA had the highest performance with an average time of 0.04 seconds.