A review on feature extraction and denoising of ECG signal using wavelet transform

Abstract

The electrocardiogram is a technique of recording bioelectric currents generated by the heart which is useful for diagnosing many cardiac diseases. The feature extraction and denoising of ECG are highly useful in cardiology. Wavelet based methods present best performance as irregularity measures and makes them suitable for ECG data analysis. This paper proposes comparison of different feature extraction and denoising techniques using wavelet transform. In an ECG with P-QRS-T wave, QRS complex has the most striking part for analysis. The first part of the paper deals with comparison of three different feature extraction techniques using wavelet transform. The second part deals with the denoising of ECG signal using three different wavelet transform. The most troublesome noise sources contain frequency components within ECG spectrum, i.e. electrical activity of the muscles and instability of electrode skin contact. Such noises are difficult to remove using typical filter procedure. In such cases signal noise reduction is only possible with wavelet denoising techniques. The comparison of different wavelet transform techniques for feature extraction and denoising of ECG signal is mentioned, which is suitable for the selection of most applicable techniques. Wavelet transform is a powerful tool for the analysis of ECG signal.