Unsupervised Identification of Cardiac Contraction through Ballistocardiography

Abstract

Ballistocardiography (BCG) is a non-invasive technique of measuring body motion generated by cardiac contractions during each cardiac cycle. In recent times BCG has caught the attention of many researchers. Several studies have attempted to leverage BCG as a non-invasive and unobtrusive method to obtain heart rate of a subject. Due to the noisy nature of BCG, most of them attempt to compute heart rate averaged over 30 second epochs. While average heart rate is an important parameter, it is not sufficient to obtain several crucial fine-grained parameters derived in time and frequency domain, such as Heart Rate Variability (HRV) which is extensively used for a deeper analysis of physiological state of the cardiovascular and autonomic nervous system. In this paper we propose a novel algorithm to detect individual heart beats from BCG data with a higher detection rate as well as accuracy better than previously proposed techniques. The proposed algorithm is able to achieve an accuracy of 93.81% for individual heart beats with a detection rate of 92.59%, and an accuracy of 98.39% for 30 second epochs with a detection rate of 99.46% compared to a standard ECG machine.