CardioFit: Affordable Cardiac Healthcare Analytics for Clinical Utility Enhancement

In this paper, we present CardioFit, a completely noninvasive cardiac condition monitoring system that enhances the clinical utility of health care analytics like lowering false detection of cardiac arrhythmia condition, higher accuracy in heart rate variability (HRV) computation. It performs powerful local analysis to enable accurate as well as easy-to-use, round-the-clock in-house, remote or mobile cardiac health checking. Here, photoplethysmogram (PPG) is the sole physiological signal considered for cardiac health management. It is to be noted that PPG carries significant necessary features what is available from electrocardiogram (ECG) signal. Unlike ECG, extraction of PPG is noninvasive, easy and affordable using smartphone or other low cost sensors. However, PPG is frequently contaminated with various kinds of motion artifacts and noise. Our robust concoction of signal processing and machine learning techniques exhibit higher accuracy in the detection and removal of the corrupt PPG signal segments. The proposed mechanism substantially improves the detection capability of the cardiac condition. Efficacy of our scheme is depicted using publicly available MIT-Physionet database as well as through our own field-collected real-life PPG data.