Detection of Cardiac Arrhythmias Using Different Neural Networks A Review

Abstract: This paper describes about the analysis of electrocardiogram (ECG) signals using neural network approach. Heart structure is a unique system that can generate ECG signals independently via heart contraction. Basically, an ECG signal consists of PQRST wave. Normal healthy heart can be simply recognized by normal ECG signal while heart disorder or arrhythmias signals contain differences in terms of features and morphological attributes in their corresponding ECG waveform. Cardiac arrhythmias are classified by abnormal activities in the heart [3]. These abnormalities can be analyzed by an electrocardiogram (ECG). Details from this electrical signal can be used to classify different types of arrhythmias. Multiple data samples of normal ECG characteristics also were read by a neural network (NN) and analyzed for the differences between abnormal signal and an irregular signal. The data was extracted from the MIT-BIH Supra ventricular database and the MIT-BIH Arrhythmia database. A neural network is designed and programmed with this data and then tested to validate the data set. When neural networks are further used to analyze and test medical data samples, the medical community and patients will experience improvements in the diagnosis of heart abnormalities and early detection of debilitating medical conditions.

Keywords: Heart, ECG signal, arrhythmia, neural network, cardiac