Discrete-wavelet-transform-based noise reduction and R wave detection for ECG signals

There are two main research topics about ECG signal proposed. One is the noise reduction, and the other is R peaks detection. Both of the two algorithms are based on discrete wavelet transform (DWT). DWT is efficient for analyzing nonstationary signals like ECG signal. The Symlets wavelets (sym5) and soft-thresholding are chosen as the wavelet function and thresholding method to do noise correction at the first denoising stage. The second stage is R wave detection. The MIT-BIH arrhythmia database is used to verify proposed algorithm. We reconstruct the decomposition level 3 to 5. Choosing the adaptive threshold and window size are the key points to reduce error rate. Applying two thresholds leads to better performance, compared to applying one threshold. At the last stage, we do noise correction again.With the information of R wave position, a novel method is proposed to eliminate the electromyogram (EMG) signal. The algorithm for R wave detection has a sensitivity of 99.70% and a positive predictivity of 99.65%. The error rate is 0.65% under all kinds of situation (0.37% if ignoring 3 worst cases). For noise correction, the SNR improvement is achieved at least 10dB at SNR 5dB, and most of the improvement SNR are better than other methods at least 1dB at different SNR. To apply presented algorithms for the portable ECG device, all R peaks can be detected no matter when people walk, run or move at the speed below 9km/hr.