A comparative study on Wavelet Transform-based algorithm for calculating Heart Rate from Ballistocardiography

Ballistocardiography (BCG) is a non-invasive, contactless technique that enables continuous monitoring of vital signals that have great importance in the healthcare domain. The BCG data acquisition system is designed to be sensitive to micro-vibrations, like the heart's mechanical beat. This makes it susceptible to noise, thus turning heartbeats detection into a complex problem. In this paper, we propose algorithms to calculate heart rate using Wavelet Transforms, which are attuned to de-noise the BCG signals and pronounce heartbeats. With the Wavelet Transforms, we used an unsupervised machine-learning algorithm to detect heartbeats accurately. The error between the proposed method and the reference electrocardiogram(ECG) is estimated in beats per minute using the mean absolute error(MAE). The best MAE achieved was 2.07, and the best detection rate of heart rate was 94.13% on a clinically acquired data set of 36 subjects. Furthermore, we have extensively tested several wavelet families on signals with varying data quality.