Rough set based segmentation and classification model for ECG

Electrical activity in the heart is given by electrocardiogram (ECG) signal. Manual analysis of ECG beat is very time consuming task as it may contain hundreds of thousands of beats for 24 hours of ECG signal. This study gives a robust classification model for ECG using Rough Set Theory (RST). RST generates rules which are simple and more apprehensible for the user causing the extraction of more accurate information from the database. ECG signal is pre-processed by using different digital filters and some essential features such as R-peak, P-wave, QRS complex etc. are extracted using signal processing toolbox in MATLAB. Finally Rough set theory is used to generate reducts and then classify the ECG signal using various classification schemes. To analyze and understand the principles of Rough Set Theory, combination of different attribute selectors, search methods and different classifier is done. We have compared different classifier such as Fuzzy Rough Nearest Neighbor , Multilayer Perception (MLP), Nearest Neighbor (NN) with respect to different parameter such as correctly classified samples, kappa statistics, root mean square error, TP rate, ROC, etc.. Performance of classifier is tested on MIT-BIH arrhythmia database.