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Patient specific and patient agnostic dictionary creation methods for compressed sensing of ECG signals were analyzed across a wide range of compression ratios using clinical metrics.
This study evaluates different dictionary learning methods for compressed sensing of ECG signals, which may improve data compression for ambulatory ECG monitoring.
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May enhance ECG compression for ambulatory monitoring; leaves open optimal dictionary method selection.
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.
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Craven et al. (2015) studied Ambulatory ECG monitoring. Compressed sensing with over-complete dictionaries was evaluated on Clinical metrics based around QRS feature extraction and heart rate variability. Patient specific and patient agnostic dictionary creation methods for compressed sensing of ECG signals were analyzed across a wide range of compression ratios using clinical metrics.
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