Performance Analysis of ECG Signal by Wavelet Transform, Independent Component Analysis and Fast Fourier Transform

ECG plays a vital role in the analysis of various heart diseases as the shape of the ECG waveform consist of vital information about heart conditions such as its electrical conduction or muscle activity. Inspite of the conventional method the extraction of ECG features is of major significance and benefit for the diagnosis of numerous harmful or even critical cardiac diseases. The feature extraction plays a vital role in diagnosis of the various cardiac diseases. Each cycle of an ECG signal contains of the P-QRS-T waves. This scheme of feature extraction describes and provides the amplitudes and intervals in the ECG signal for further investigation. The amplitudes and intervals value of P-QRS-T segment shows the operation of heart. Recently, various techniques have been evolved for analysis of the ECG signal. This paper discusses three most widely used methods used to extract the different features of Electrocardiograph (ECG) signals namely Wavelet Transform (WT), Fast Fourier Transform (FFT), Independent Component Analysis (ICA). The study conveys the information that the Fast Fourier Transform method gives better performance in frequency domain for the ECG feature extraction. Accuracy of Wavelet Transform is 92.20%, of the Fast Fourier Transform is 92.47%, and of the Independent Component Analysis is 90.13%. It has been observed that FFT shows better performance regarding the ECG signal analysis. Moreover, provides efficient estimation of the PSD from noise corrupted signals. But the limitation of this method is the leakage decreases the ability of FFT to resolve two frequencies of close space. But by the use of a window function will reduce this leakage.