While cardiovascular diseases (CVDs) are prevalent across economic strata, the economically disadvantaged population is disproportionately affected due to the high cost of traditional CVD management, involving consultations, testing and monitoring at medical facilities. Accordingly, developing an ultra-low-cost alternative, affordable even to groups at the bottom of the economic pyramid, has emerged as a societal imperative. Against this backdrop, we propose an inexpensive yet accurate home-based electrocardiogram (ECG) monitoring service. Specifically, we seek to provide point-of-care monitoring of premature ventricular contractions (PVCs), high frequency of which could indicate the onset of potentially fatal arrhythmia. Note that the first-generation telecardiology system acquires the ECG, transmits it to a professional diagnostic center without processing, and nearly achieves the diagnostic accuracy of a bedside setup. In the process, such a system incurs high bandwidth cost and requires the physicians to process the entire record for diagnosis. To reduce cost, current telecardiology systems compress data before transmitting. However, the burden on physicians remains undiminished. In this context, we develop a dictionary-based algorithm that reduces not only the overall bandwidth requirement, but also the physicians workload by localizing anomalous beats. Specifically, we detect anomalous beats with high sensitivity and only those beats are then transmitted. In fact, we further compress those beats using class-specific dictionaries subject to suitable reconstruction/diagnostic fidelity. Finally, using Monte Carlo cross validation on MIT/BIH arrhythmia database, we evaluate the performance of the proposed system. In particular, with a sensitivity target of at most one undetected PVC in one hundred beats, and a percentage root mean squared difference less than 9% (a clinically acceptable level of fidelity), we achieved about 99.15% reduction in bandwidth cost, equivalent to 118-fold savings over first-generation telecardiology. In the process, the professional workload is reduced by at least 85.9% for noncritical cases. Our algorithm also outperforms known algorithms under certain measures in the telecardiological context.
