Diagnosis of ECG data for detecting cardiac disorder using DP-matching and artificial neural network

Computational Intelligence has made a huge impact on solving many complicated problem particularly in the medical field. With the advancement of computational intelligence where the effectiveness of data analysis is at high stake, the process of classifying and interpreting data accurately based on logical reasoning in decision making is not a big issue. This study discusses the process of diagnosing cardiac disorder using computational intelligence with specific focus on the feature extraction where the attribute of identifying Normal Sinus and Atrial Fibrillation rhythms using Physionet.org database is examined. In this paper, an algorithm to diagnose the cardiac disorder based on DP-Matching will be proposed where the time and frequency domains of ECG signal segments are introduced. At the end of this paper, the performance evaluations of the proposed method will be shown with the analysis by ANN.