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An Algorithm for Automatic QRS Delineation Based on ECG-gradient Signal

Abstract:

In this work, an algorithm based on digital signal processing and machine learning is developed for QRS complexes detection in ECG signals. The algorithm for locating the complexes uses a gradient signal and the KNN classification method. In the first step, an efficient process for denoising signals using Stationary Wavelet Transform (SWT), Discrete Wavelet Transform (DWT), and a combination of filtering thresholds is developed. In the second stage, the phase of fiducial points detection is carry out, the gradient of the signal is computed for being used as a feature for the detection of the R-peak. Therefore, a KNN classification method is used in order to separate R-peaks and non R-peaks. The algorithm computes a set of thresholds to recalculate the R-peaks positions that has been omitted or falsely detected due to the ECG wave forms. Finally, the each R peak permits locate Q and S peaks. The results indicate that the algorithm correctly detects 99.7 % of the QRS complexes for the MIT-BIH Arrhythmia database and the 99.8 % using the QT Database. The average processing-time that the algorithm takes to process a signal from the denoising stage to fiducial points detection is 4.95 s.