A comparison of three heart rate detection algorithms over ballistocardiogram signals

Heart rate (HR) detection from ballistocardiogram (BCG) signals is challenging for various reasons. For example, BCG signals' 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 different datasets and under different experimental setups. This paper investigated the suitability of three algorithms (i.e., MODWT-MRA, CWT, and template matching) for HR detection across three independent BCG datasets. The first two datasets (Datset1 and DataSet2) were obtained using a microbend fiber optic (MFOS) sensor, while the last one (DataSet3) was obtained using a fiber Bragg grating (FBG) sensor. DataSet1 was collected from 10 OSA patients during an in-lab PSG study, Datset2 was obtained from 50 subjects in a sitting position, and DataSet3 was gathered from 10 subjects in a sleeping position. The CWT with derivative of Gaussian (Gaus2) provided superior results than the MODWT-MAR, CWT (frequency B-spline-Fbsp-2-1-1), and CWT (Shannon-Shan1.5-1.0) for DataSet1 and DataSet2. That said, a BCG template was constructed from DataSet1. Then, it was applied for HR detection across DataSet2. The template matching method achieved slightly superior results than CWT-Gaus2 for DataSet2. Furthermore, it has proved useful for HR detection across DataSet3 despite that BCG signals were obtained from a different sensor and under different conditions. Overall, the time required to analyze a 30-second BCG signal was in a millisecond resolution for the three proposed methods. The MODWT-MRA had the highest performance, with an average time of 4.92 ms.