A comparison of three heart rate detection algorithms over ballistocardiogram signals. (September 2021)

• Record Type: Journal Article
• Authors: Sadek, Ibrahim; Abdulrazak, Bessam
• Is Part Of: Biomedical signal processing and control. Volume 70(2021)
• Publication Date: 2021-09
• Subjects: Ballistocardiography -- Mobile health -- Homecare -- Heart rate -- Wavelet transform -- Template matching; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103017
• Languages: English
• ISSNs: 1746-8094

Abstract

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 were 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 s.