Cardiac Disorder Diagnosis Based on ECG Segments Analysis and Classification

In this study we present a new approach to analyze and classify ECG signals for diagnosis of five cardiac conditions. Instead of following the conventional approach of beat-to-beat classification, we classify cardiac rhythms/segments of ECG based on statistical and morphological features extracted from them. We select and extract seven suitable features and reduce our feature space by using PCA and LDA to optimize our problem. In classification phase, we use two types of classifiers, i.e. Euclidean and Manhalanobis and compare their results. A new algorithm for segmentation of ECG lengths is also presented. This study provides a fundamental step for the development of preliminary automated diagnostic system for cardiac disorders.