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An Integrated Statistical Process Control and Wavelet Transformation Model for Detecting QRS Complexes in ECG Signals

An Integrated Statistical Process Control and Wavelet Transformation Model for Detecting QRS Complexes in ECG Signals

Wen-Hung Yang, Bernard C. Jiang
Copyright: © 2010 |Volume: 1 |Issue: 2 |Pages: 20
ISSN: 1947-3087|EISSN: 1947-3079|EISBN13: 9781609604257|DOI: 10.4018/jalr.2010040101
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MLA

Yang, Wen-Hung, and Bernard C. Jiang. "An Integrated Statistical Process Control and Wavelet Transformation Model for Detecting QRS Complexes in ECG Signals." IJALR vol.1, no.2 2010: pp.1-20. http://doi.org/10.4018/jalr.2010040101

APA

Yang, W. & Jiang, B. C. (2010). An Integrated Statistical Process Control and Wavelet Transformation Model for Detecting QRS Complexes in ECG Signals. International Journal of Artificial Life Research (IJALR), 1(2), 1-20. http://doi.org/10.4018/jalr.2010040101

Chicago

Yang, Wen-Hung, and Bernard C. Jiang. "An Integrated Statistical Process Control and Wavelet Transformation Model for Detecting QRS Complexes in ECG Signals," International Journal of Artificial Life Research (IJALR) 1, no.2: 1-20. http://doi.org/10.4018/jalr.2010040101

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Abstract

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.

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