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Adaptive Threshold, Wavelet and Hilbert Transform for QRS Detection in Electrocardiogram Signals

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Advances on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC 2017)

Abstract

This paper combines Hilbert and Wavelet transforms and an adaptive threshold technique to detect the QRS complex of electrocardiogram signals. The method is performed in a window framework. First, the Wavelet transform is applied to the ECG signal to remove noise. Next, the Hilbert transform is applied to detect dominant peak points in the signal. Finally, the adaptive threshold technique is applied to detect R-peaks, Q, and S points. The performance of the algorithm is evaluated against the MIT-BIH arrhythmia database, and the numerical results indicated significant detection accuracy.

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Acknowledgments

This project is supported by Research Grant No. DSA/103.5/16/10473 awarded by PRODEP and the Autonomous University of Ciudad Juarez. Title - Detection of Cardiac Arrhythmia Patterns through Adaptive Analysis.

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Correspondence to Ricardo Rodriguez Jorge .

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Rodriguez Jorge, R., García, E.M., Córdoba, R.T., Bila, J., Mizera-Pietraszko, J. (2018). Adaptive Threshold, Wavelet and Hilbert Transform for QRS Detection in Electrocardiogram Signals. In: Xhafa, F., Caballé, S., Barolli, L. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-319-69835-9_73

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  • DOI: https://doi.org/10.1007/978-3-319-69835-9_73

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69834-2

  • Online ISBN: 978-3-319-69835-9

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