Continuous Wavelet Transform-Based Ballistocardiogram Beat Detection for RR Interval Estimation

Ballistocardiography (BCG) is one of various noninvasive methods used for measuring the cardiac’s activity. On the contrary to electrocardiography (ECG), ballistocardiography is a method based on the measurement of the human body motion caused by cardiac activity. BCG signals have been applied for assessing and monitoring the heart’s health. A beat detection is one of the most fundamental processing stages. In this study, a computational algorithm based on the continuous wavelet transform (CWT) is developed for the ICBHI2022 Scientific Challenge. The main goal of the ICBHI2022 Scientific Challenge is to estimate the RR intervals using a BCG signal. The performance on RR interval estimation is determined using the global score that is obtained from the difference between RR intervals estimated from detected BCG beats and actual RR intervals. The CWT-based BCG beat detection proposed is composed of two main steps: 1) CWT-based filtering; and 2) peak detection. From all ten entries in the Phase II of the ICBHI2022 Scientific Challenge, the best global score achieved using the proposed CWT-based BCG beat detection is 133.41 with the upper frequency \(f_u\) , the lower frequency \(f_l\) , and the minimum peak distance d of 6.6 Hz, 4.2 Hz, and 109 samples, respectively.