2021/01/31 by Ibrahim Sadek, Bessam Abdulrazak
Engineering · Mathematics · Medicine · Physics and Astronomy · #Algorithm #Artificial intelligence #Computer science #Computer vision #Continuous wavelet transform #Discrete wavelet transform #ECG Monitoring and Analysis #Heart Rate Variability and Autonomic Control #Mathematics #Non-Invasive Vital Sign Monitoring #Pattern recognition (psychology) #Position (finance) #SIGNAL (programming language) #Wavelet #Wavelet transform #eess.SP #physics.med-ph
paper · pdf · doi:10.1016/j.bspc.2021.103017
Preprint submitted to Biomedical Signal Processing and Control Journal
openalex created_date 2021/02/01 · arxiv created 2021/07/21 · openalex publication_date 2021/08/10 · arxiv updated 2021/08/11 · openalex updated_date 2026/08/05
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.