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A New QRS Detection Method Using Wavelets and Artificial Neural Networks

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Abstract

We present a new method for detection and classification of QRS complexes in ECG signals using continuous wavelets and neural networks. Our wavelet method consists of four wavelet basis functions that are suitable in detection of QRS complexes within different QRS morphologies in the signal and thresholding technique for denoising and feature extraction. The results demonstrate that the proposed method is not only efficient for normal ECG signal analysis but also for various types of arrhythmic cardiac signals embedded in noise. For the classification stage, a feedforward neural network was trained with standard backpropagation algorithm. The classifier input features consisted of compact wavelet coefficients of QRS complexes that resulted in higher classification rates. We demonstrate the efficiency of our method with the average accuracy 97.2% in classification of normal and abnormal QRS complexes.

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Acknowledgments

This research was supported by Yeungnam University research grants. We would like to thank the anonymous reviewers for their assistance and for comments that greatly improved the manuscript.

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Authors and Affiliations

  1. Department of Electronic Engineering, Yeungnam University, Gyeongsan, South Korea, 712–749

    Berdakh Abibullaev

  2. Department of Electronic Engineering, Yeungnam University, Dae-dong 214-1, Gyeongsan, Gyeongbuk, 712–749, Korea

    Hee Don Seo

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  1. Berdakh Abibullaev
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  2. Hee Don Seo
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Correspondence to Hee Don Seo.

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Abibullaev, B., Seo, H.D. A New QRS Detection Method Using Wavelets and Artificial Neural Networks. J Med Syst 35, 683–691 (2011). https://doi.org/10.1007/s10916-009-9405-3

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  • DOI: https://doi.org/10.1007/s10916-009-9405-3

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