Abstract
Heart is one of the most important organs in the human body and disorders in its functioning can cause serious problems. Arrhythmias are abnormal heart beats. In fact, arrhythmias are heart diseases, caused by heart electrical-conductive system disorders. They are characterized with very slow (bradycardia) or very fast (tachycardia) heart functions resulting in an inefficient pumping. The heart state is generally reflected in the shape of ECG waveform and heart rate. Various computer-based methodologies for automatic diagnosis have been proposed by researchers; however the entire process can generally be subdivided into a number of separate processing modules such as preprocessing, feature extraction/selection, and classification.
In this research we focus on filtering the ECG signal in order to remove high frequency noise and enhance the QRS complexes, and on feature extraction. The latter is the determination of a feature vector from the ECG pattern vector. Our feature selection approach is based on implementation of orthonormal functions. Representing ECG morphology with coefficients of orthonormal polynomials results in robust estimates of a few descriptive signal parameters. Exposition of subtle features of normal and deviating ECG pattern vectors allows their accurate representation. The experimental data includes recordings from MIT dataset.