Artificial Intelligent Based Arrhythmia Identification Via Single Lead Ecg Recording - Lim, Guo Jin (Universiti Sains Malaysia, 2017)
Electrocardiogram (ECG) represents the electrical activities of our heart. It provides various information about our heart status such as cardiac disorder or arrhythmia. ECG has become the most common diagnostic tool in heart analysis as well as in monitoring for cardiac problem. In the past century, arrhythmia has become the most common heart disease, showing the least symptoms while having the greatest effect toward the victims. Despite the plenty of studies that have been done in Arrhythmia detection, it problematic as Arrhythmia may only happen periodically. The main goal of this study is to develop an artificial neural network based algorithm which is able to classify the ECG rhythm. At the first stage, the ECG signal is classified into noisy ECG and clean ECG. Only clean ECG signal will be fetched into the second stage to be classified into Arrhythmia or Normal Sinus rhythm. Different features have been used in both stages and been fetched into trained MLP neural network for classification purpose. At first stage classification, 6 features have been selected as input and 15 number of neurons in hidden layer have been used. Meanwhile at the second stage, 4 features have been selected as input and 40 numbers of hidden layer’s neuron has been used. Final accuracy of 83.3% has been achieved during the training stage by using 300 training data. Final score of 0.7076 (Perfect score = 1) has been achieved when the 8528 data has been fetched into the developed neural network. In conclusion, suitable features have been identified which are average and standard deviation of heart rate and R-peak amplitude. Finally, a high accuracy neural network has been developed in this study.