ECG CLASSIFICATION USING NEURAL NETWORK

Abstract

An electrocardiogram (ECG) is one of the biomedical signals that is considered a very useful approach to providing information about heart problems. This thesis has been done to contribute to making machines of observation of hearts have more ability for making accurate and fast diagnosis so that life of more patients might be saved. Physios Bank was the source of our dataset. It has many real examples of heart diseases that we can choose for our studies. In this research, there are five heart cases that were used for this research, normal N, atrial premature beat PAC, premature ventricular contraction PVC, left bundle branch block beat LBBB, and right bundle branch block beat RBBB. Classifying these five cases with a high efficiency and accuracy using neural network is our final goal. To achieve this goal, ECG signals must go through specific procedures or steps. The first procedure was ECG signal preprocessing. This step has three sup steps, signal filtering, signal detrending, and signal smoothing. The second procedure is extracting features of ECG signals. The forth one is classifying ECG signals using neural network. Finally, the results of NN will be saved for future purposes. Our system was implemented by using MATLAB because it is a very powerful software for signal processing and signal analysis. Our research was ended with some good achievements and optimizations. For example, discovering good techniques for filtering, finding new way for features extraction, building one neural network to classify multiple heart diseases, and making a high accuracy with 96.88% percent.