Supervised and unsupervised learning for diagnostic ECG classification

A hybrid system with RBF pre-processing, a system with supervised learning, is compared with some Kohonen self-organizing maps in a subtle ECG classification task. Based on ECG measures, they are supposed to detect normal condition, presence of infarction and of hypertrophy, and at the same time to sub-classify those pathologies. During the evaluation process the hybrid system produces better results. In terms of average sensitivity and specificity (83% vs. 62% of sensitivity and 84% vs. 92% of specificity), but Kohonen maps allow a detailed description of the similarities among input data. An integration of the two techniques should improve the final results.