Best basis segmentation of ECG signals using novel optimality criteria
Automatic segmentation of the electrocardiogram (ECG) is important in both clinical and research settings. Past algorithms have relied on incorporation of detailed heuristics. We avoid heuristics by employing a best-basis algorithm. As large variability of the local SNR causes the standard entropy criterion to produce an overly-fine segmentation, we introduce a novel optimality criterion which is based on a linear combination of the entropy measure and a function of a smoothness measure, and is quite general in form. We tested the algorithm on the MIT-BIH arrythmia database and body surface potential maps.