An application of MCMC methods for the multiple change-points problem
Signal Processing - Special section on Markov Chain Monte Carlo (MCMC) methods for signal processing
Numerical performance of block thresholded wavelet estimators
Statistics and Computing
Computer Methods and Programs in Biomedicine
Hyper-trim shrinkage for denoising of ECG signal
Digital Signal Processing
Expert Systems with Applications: An International Journal
Combining Algorithms in Automatic Detection of QRS Complexes in ECG Signals
IEEE Transactions on Information Technology in Biomedicine
De-noising by soft-thresholding
IEEE Transactions on Information Theory
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In this study, the authors propose an approach for detecting R-wave of electrocardiogram (ECG) signals. A statistical process control chart is successfully integrated with wavelet transformation (WT) to detect R-wave locations. This chart is a graphical display of the quality characteristic measured or computed from samples versus the sample number or time from the production line in a factory. This research performed WT at the signal preprocessing stage; the change points and control limits are then determined for each segment and the R-wave location is rechecked by spreading the points at the decision stage. The proposed procedures determine the change points and control limits for each segment. This method can be used to eliminate high-frequency noise, baseline shifts and artifacts from ECG signals, and R-waves can be effectively detected. In addition, there is flexibility in parameter value selection and robustness over wider noise ranges for the proposed QRS detection method.