Impact of compressed sensing on clinically relevant metrics for ambulatory ECG monitoring. Issue 4 (1st February 2015) - Craven, D.; McGinley, B.; Kilmartin, L.; Glavin, M.; Jones, E.
Abstract : Recent research has examined the combination of compressed sensing with over‐complete dictionaries for the lossy compression of electrocardiogram (ECG) signals. The application of dictionary learning to automatically create the dictionary is described. A novel analysis of the reconstructed signals using a range of clinical metrics based around QRS feature extraction and heart rate variability is employed. Two methods for dictionary creation are proposed: patient specific and patient agnostic. A detailed comparison of each approach is described. Considering ambulatory ECG monitoring as an application, each methodology is analysed for a wide range of compression ratios.