2017/05/24 by Bollepalli S. Chandra, C. S. Sastry, Chandra, Bollepalli S. +6
Computer Science · Engineering · Medicine · #Cardiac electrophysiology and arrhythmias #ECG Monitoring and Analysis #FOS: Computer and information sciences #Healthcare Technology and Patient Monitoring #Machine Learning (cs.LG) #Non-Invasive Vital Sign Monitoring #cs.LG
paper · pdf · doi:10.48550/arxiv.1705.08619
19 pages, 9 figures and 5 tables
arxiv created 2017/05/24 · openalex publication_date 2017/05/24 · arxiv updated 2017/05/25 · openalex created_date 2022/10/02 · openalex updated_date 2026/07/28
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. 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 a traditional telecardiology system acquires the ECG, transmits it to a professional diagnostic centre without processing, and nearly achieves the diagnostic accuracy of a bedside setup, albeit at high bandwidth cost. In this context, we aim at reducing cost without significantly sacrificing reliability. To this end, we develop a dictionary-based algorithm that detects with high sensitivity the anomalous beats only which are then transmitted. We further compress those transmitted beats using class-specific dictionaries subject to suitable reconstruction/diagnostic fidelity. Such a scheme would not only reduce the overall bandwidth requirement, but also localising anomalous beats, thereby reducing physicians' burden. 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 traditional telecardiology.