Multi-channel ECG classification using forests of randomized shapelet trees
Isak Karlsson, Panagiotis Papapetrou, Lars Asker · 2015
Data series of multiple channels occur at high rates and in massive quantities in several application domains, such as healthcare. In this paper, we study the problem of multi-channel ECG classification. We map this problem to multivariate data series classification and propose five methods for solving it, using a split-and-combine approach. The proposed framework is evaluated using three base-classifiers on real-world data for detecting Myocardial Infarction. Extensive experiments are performed on real ECG data extracted from the Physiobank data repository. Our findings emphasize the importance of selecting an appropriate base-classifier for multivariate data series classification, while demonstrating the superiority of the Random Shapelet Forest (0.825 accuracy) against competitor methods (0.664 accuracy for 1-NN under cDTW).