ANALYSIS OF CARDIAC ARRHYTHMIAS USING NEURAL NETWORKS

The electrocardiogram (ECG) is the recording of the electrical potential of heart versus time. The analysis of ECG signal has great importance in the detection of cardiac abnormalities. The electrocardiographic signals are often contaminated by noise from diverse sources. Noises that commonly disturb the basic electrocardiogram are power line interference, instrumentation noise, external electromagnetic field interference, noise due to random body movements and respirational movements. These noises can be classified according to their frequency content. It is essential to reduce these disturbances in ECG signal to improve accuracy and reliability. Proposed research works offers ECG signal classification system uses Principal Component Analysis (PCA) technique to reduce the dimensionality of test signal. Discrete Wavelet Transform is used for feature extraction. Spectral flatness is another feature for the spectrum of ECG. This process helps in enhancing the classification accuracy. Classification is done using Neural Network classifier