Arrhythmia Classification from ECG signals using Data Mining Approaches

The objective of this paper is to develop a model for ECG (electrocardiogram) classification based on Data Mining techniques. The MITBIH Arrhythmia database was used for ECG classical features analysis. This work is divided into two important parts. The first parts deals with extraction and automatic analysis for different waves of electrocardiogram by time domain analysis and the second one concerns the extraction decision making support by the technique of Data Mining for detection of EGC pathologies. Two pathologies are considered: atrial fibrillation and right bundle branch block. Some decision tree classification algorithms currently in use, including C4.5, Improved C4.5, CHAID and Improved CHAID are performed for performance analysis. The bootstrapping and the cross-validation methods are used for accuracy estimation of these classifiers designed for discrimination. The Bootstrap with pruning by 5 attributes achieves the best performance managing to classify correctly.