Reliable low-cost telecardiology: High-sensitivity detection of ventricular beats using dictionaries

Abstract

Can reliable telecardiology be achieved at low bandwidth cost? In response, we propose a detector at the user end so that only beats found to be anomalous are transmitted to a diagnostic center, where all received beats are correctly (re)classified. In this framework, high reliability is achieved by detectors with high sensitivity. Having laid the design framework, we then realize desired high-sensitivity detection using a dictionary learning approach. Specifically, using patient records from the MIT-BIH arrhythmia database, we detect ventricular ectopic beats (VEBs), which are known to be precursors to various serious arrhythmic conditions in the heart. In particular, we achieve a reliability of one undetected VEB in one thousand while saving 78.2% bandwidth using dictionaries with 240 atoms. With larger dictionaries with 420 atoms, we achieve an even higher bandwidth savings of 79.2% while allowing no (less than one in 1766) undetected VEB. Finally, we compare our results with performances a large set of reported heartbeat classifiers, and demonstrate the suitability of our approach in the context of telecardiology.