Detection of abnormal heart conditions based on characteristics of ECG signals

Abstract

Heart diseases are one of the most important death causes across the globe. Therefore, early detection of heart diseases is crucial to reduce the rising death rate. Electrocardiogram (ECG) is widely used to diagnose many types of heart diseases such as abnormal heartbeat rhythm (arrhythmia). However, the non-linearity and the complexity of the abnormal ECG signals make it very difficult to detect its characteristics. Besides, it may be time-consuming to check these ECG signals manually. To overcome these limitations, we have proposed fast and accurate classifier that simulates the diagnosis of the cardiologist to classify the ECG signals into normal and abnormal from a single lead ECG signal and better than other well-known classifiers. First, an accurate algorithm is used for correcting the ECG signals from noise and extracting the major features of each ECG signal. After that, we simulated the characteristics of the ECG signals and created the proposed classifier from these characteristics. Two Neural Network (NN) classifiers, four Support Vector Machine (SVM) classifiers and K-Nearest Neighbor (KNN) classifier are employed to classify the ECG signals and compared with the proposed classifier. The total 13 features extracted from each ECG signal used in the proposed algorithm and set as input to the other classifiers. Our algorithm is validated using all records of MIT-BIH arrhythmia database. Experimental results show that the proposed classifier demonstrates better performance than other classifiers and yielded the highest average classification accuracy of 99%. Thus, our algorithm has the possibility to be implemented in clinical settings.