Key result
A Gaussian dictionary-based signal analysis algorithm achieved 90% data compression rates on ECG recordings while maintaining a percent relative deviation of reconstructed traces below 5%.
Why the study?
ECG trace data streaming over wireless links in wearable cardiac monitors creates significant challenges due to the large volume of data to be transmitted.
A Gaussian dictionary-based algorithm for ECG trace compression achieves 90% data compression rates with less than 5% reconstruction error, enabling efficient wireless streaming for wearable monitors.
May enable efficient wireless ECG streaming from wearables; leaves open clinical validation for arrhythmia detection.
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, due to the amount of data to be transmitted. We employ a signal analysis approach based on a Gaussian dictionary to model ECG traces in a compressed way. The algorithm operates on fixed-length segments, and achieves effective compression for wireless data transmission, associating just 6 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 Arrhythmia Database, obtaining 90% data compression rates, while percent relative deviation of reconstructed traces is always below 5%.
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Galli et al. (2023) studied Arrhythmia (n=46). Gaussian feature streaming algorithm vs. Original uncompressed ECG traces was evaluated on Data compression rate and Percent Relative Deviation (PRD). A Gaussian dictionary-based signal analysis algorithm achieved 90% data compression rates on ECG recordings while maintaining a percent relative deviation of reconstructed traces below 5%.
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