Gaussian-based analysis for accurate compressed ECG trace streaming

Abstract

Wearable cardiac monitors can usefully contribute to early detection of potential cardiovascular pathologies, however ECG trace data streaming over wireless links creates some significant challenges. We propose a signal analysis approach based on a Gaussian dictionary to model and compress ECG traces. The algorithm operates on fixed-length segments, and achieves effective compression for wireless data transmission, associating just 10 bytes to each Gaussian feature. At the same time it enables accurate reconstruction of ECG traces from the reduced data set. We tested our method on a set of 46 ECG recordings taken from the Physionet MIT-BIH Arrythmia Database, obtaining 90% data compression rates, while percent relative deviation of reconstructed traces is always below 5%.