Wavelet transform based noise removal from ECG signal for accurate heart rate detection using ECG

Use of wavelet transform for the analysis of bioelectric signals has gained momentum and effective results have been obtained through this method. In this study, we have applied the wavelet analysis to the electrocardiogram (ECG) signal for the better detection of heart rate. Wavelet analysis is used for the elimination of baseline wandering and elimination of the highfrequency noise. Heart rate detection is performed on this final signal through simple R peak determination. The data used in this study is taken from the MIT-BIH arrhythmia database which is chosen because of the labelled the R-peaks of the QRS complexes. When the raw data were analyzed for the R -peaks, detection average rate of R peaks was 94.87%. In the algorithm, the ECG signal was first decomposed up to 4th level through wavelet analysis. The fourth level approximation coefficient was subtracted from the ECG signal to eliminate the baseline wandering. The new signal was again passed through wavelet packet filters up to 2nd level. The detail coefficient at the 2nd level was subtracted from this signal for the elimination of high frequency noise. For the Daubechies 4 (db4), Daubechies 10 (db10) and Coiflet 4 (coif4) wavelet families, average detection rate of R peaks exceeded 99%. Selection of db10 for the wavelet filters in the algorithm gave the best results with detection rate of 99.76%.