Extreme learning machine approach on heart abnormalities identification in ECG images

Abstract

Heart abnormalities are atypical heart conditions that can lead to chronic heart disease. Heart abnormalities can be severe if not treated directly due to the crucial function of the heart as the blood circulation center. Heart abnormalities cannot be seen with the naked eye so it requires the recording of a heartbeat wave or electrocardiogram (EKG) for the disease to be detected. Therefore, a strategy that uses image processing and artificial neural networks to detect anomalies in the heart is strongly advocated. The proposed methods for feature extraction and identification are Invariant Moments and Extreme Learning Machine respectively. The testing procedure for this research employed a total of 386 ECG images as training data. and 44 ECG images for test data, and the heart condition was classified into 4 classes, namely Atrial Fibrillation, T-Wave, ST-Segment, and normal heart conditions. The test was carried out using 3 choices of extreme learning machine activation functions, namely sigmoidal, sine and hard-lim. The test also applied the parameter of hidden neurons in which amounting to 10, 30, 50, 100 and 500. The system accuracy in identifying heart abnormalities achieved 95.45% by the application of the sigmoid function with the total number of hidden neurons equal to 500.