Tree-Based Classification of ECG Signal Associated with DWT Features

Nowadays, Electrocardiogram (ECG) is one of the most widely used techniques for diagnosing cardiac diseases. The system presents the methods to analyze electrocardiogram (ECG) signal, detect the QRS complex, and extract the morphological features and temporal features according to the different data. Firstly, the input ECG signal is often contaminated by noise. In order to extract useful information from the noisy ECG signals, the raw ECG signal has to be preprocessed. The baseline wandering is significant and can strongly affect ECG signal analysis. In the preprocessing stage, the input noisy ECG signal has been eliminated with the powerful high and low pass filters. Then, the extracted features from the ECG signals achieve using Discrete Wavelet Transform. The system classifies the heart beats types on the extracted features using Tree-based classification. Data are obtained from the records of the MIT-BIH database. The implementation of the approach is accomplished using Matlab 2016a programme software and the Experimental results for the system quality is measured the accuracy of the system.