A New QRS Detection Method Using Wavelets and Artificial Neural Networks

Authors:
Berdakh Abibullaev;Hee Don Seo
Affiliations:
Department of Electronic Engineering, Yeungnam University, Gyeongsan, South Korea 712---749;Department of Electronic Engineering, Yeungnam University, Gyeongsan, Gyeongbuk, Korea 712---749
Venue:
Journal of Medical Systems
Year:
2011

Citing 3
Cited 1

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De-noising by soft-thresholding

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A ±6ms-accuracy, 0.68mm2, and 2.21 µW QRS detection ASIC

VLSI Design

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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.