Implementation of Back Propagation Artificial Neural Network for Heart Disease Abnormality Diagnosis

Abstract

Heart is a very important human organ, where in normal people heart beats at 60-100 beats per second, but there are abnormalities in the heart rate that can occur due to certain causes so that it becomes slower (bradycardia) or faster (tachycardia). Electrical activity of the heart can be detected by an electrocardiogram (ECG), where the output of this device is a signal that describes the condition of a person’s heart. Artificial neural network (ANN) is one of the learning methods of artificial intelligence that can be used for pattern recognition or the other. One of the ANN learning paradigms is backpropagation where the computation goes through 2 stages, namely advanced calculation and backward calculation. This study aims to simulate backpropagation neural networks to recognize patterns from the output of the electrocardiogram using the MATLAB program. The input is form of printed electrocardiogram recording, and then it is normalized, next the data is processed by backpropagation computing with two phases (training phase and testing phase). The output of this ANN is a description of a patient’s condition whether normal, bradycardia or tachycardia.