De-Noising and Statistical Feature Extraction of the ECG Signal Using Wavelet Analysis
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 de-noising of ECG are highly useful in cardiology. And
baseline wander elimination is considered as a traditional problem. So this paper deals with the noise removal and feature
extraction of the signal. This baseline wander noise is removed by Butterworth filter. We present a wavelet-transform (WT)
based search algorithm. The algorithm computes wavelet packet coefficients and then in each scale the different nine features
of the signal is calculated. Comparison is made between the characteristics of signals and the branch of the wavelet binary
tree corresponding to minimum entropy wavelet spaces is chosen. This algorithm is tested using the data record from
MIT/BIH database for arrhythmia disease categorized into normal and abnormal and excellent results are obtained.
Keywords—ECG signal, de-noising, feature extraction, MIT database, wavelet packet decomposition.