Accurate ECG monitoring by Gaussian feature streaming

• Published in: Measurement : journal of the International Measurement Confederation Vol. 223; p. 113757
• Main Authors: Galli, Alessandra, Giorgi, Giada, Narduzzi, Claudio
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2023
• Subjects: Compression; Electrocardiogram; Fixed segmentation; Gaussian dictionary; Morphological feature set; Wearable monitoring; Fixed segmentation; Compression; Morphological feature set; Gaussian dictionary; Electrocardiogram; Wearable monitoring
• ISSN: 0263-2241
• DOI: 10.1016/j.measurement.2023.113757

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%. •Gaussian kernels enable accurate parsimonious ECG morphological modelling.•Compact feature set facilitates remote wireless ECG monitoring.•Data streams are very low bit-rate, yet allow high-accuracy ECG reconstruction.•Fixed segmentation decouples trace analysis from ECG-related features.•Dictionary-based analysis supports comparability and repeatability in ECG analysis.