Abstract
Electrocardiogram (ECG) plays an enormous role in the medical field. An electrocardiograph is a device used in cardiology, which records heart’s electrical signals over time. ECG can be used to determine various heart diseases or damages to the heart along with the pace at which the heart beats as well as the effects of drugs or devices used to control the heart. The interpretation of the ECG signals is an application of pattern recognition. The technique used in this project integrates the study of the ECG signals and their classification. Analysis of ECG signals is done using neural network pattern recognition and classification methods. The study includes simulation of ECG signals, comparison between ECG signals, and detection of any abnormalities in the signal by using effective learning algorithms & pattern recognition techniques. The processed signals used in this project are obtained from an arrhythmia database, which was developed for research in cardiac electrophysiology by Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH). The neural clustering application available in the pattern recognition tool software is used to classify ECG signals based on self-organizing maps. Self-organizing maps are used to cluster the data, based on the similarity and topology, which reduces the dimensionality of the data. Thus, after training the network using the classification tool, a given ECG signal can be classified as normal or arrhythmic signal based on its features.