J Med Syst - A new QRS detection method using wavelets and artificial neural networks.

Tópicos

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Resumo

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.

Resumo Limpo

present new method detect classif qrs complex ecg signal use continu wavelet neural network wavelet method consist four wavelet basi function suitabl detect qrs complex within differ qrs morpholog signal threshold techniqu denois featur extract result demonstr propos method effici normal ecg signal analysi also various type arrhythm cardiac signal embed nois classif stage feedforward neural network train standard backpropag algorithm classifi input featur consist compact wavelet coeffici qrs complex result higher classif rate demonstr effici method averag accuraci classif normal abnorm qrs complex

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