Automatic Detection of Heart Disease Using Discreet Wavelet
ECG plays an important role for analysis and diagnosis of heart disease. ECG signals are affected by different noises. These noises can be removed by de noise the ECG signal. After de noising ECG signals, a pure ECG signal is used to detect ECG parameters. Then Feature extraction of ECG signal is carried out by DWT techniques which are applied to ANN for classification to detect cardiac arrhythmia. This paper introduces the Electrocardiogram (ECG) pattern recognition method based on wavelet transform and neural network technique has been used to classify two different types of arrhythmias, namely, Left bundle branch block (LBBB), Right bundle Branch block (RBBB) with normal ECG signal. The MIT-BIH arrhythmias ECG Database has been used for training and testing our neural network based classifier. The simulation results given at the end. KeywordsECG, DWT, ANN, RBBB, LBBB.