Comput Methods Programs Biomed - Neural network and wavelet average framing percentage energy for atrial fibrillation classification.

Tópicos

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Resumo

ECG signals are an important source of information in the diagnosis of atrial conduction pathology. Nevertheless, diagnosis by visual inspection is a difficult task. This work introduces a novel wavelet feature extraction method for atrial fibrillation derived from the average framing percentage energy (AFE) of terminal wavelet packet transform (WPT) sub signals. Probabilistic neural network (PNN) is used for classification. The presented method is shown to be a potentially effective discriminator in an automated diagnostic process. The ECG signals taken from the MIT-BIH database are used to classify different arrhythmias together with normal ECG. Several published methods were investigated for comparison. The best recognition rate selection was obtained for AFE. The classification performance achieved accuracy 97.92%. It was also suggested to analyze the presented system in an additive white Gaussian noise (AWGN) environment; 55.14% for 0dB and 92.53% for 5dB. It was concluded that the proposed approach of automating classification is worth pursuing with larger samples to validate and extend the present study.

Resumo Limpo

ecg signal import sourc inform diagnosi atrial conduct patholog nevertheless diagnosi visual inspect difficult task work introduc novel wavelet featur extract method atrial fibril deriv averag frame percentag energi afe termin wavelet packet transform wpt sub signal probabilist neural network pnn use classif present method shown potenti effect discrimin autom diagnost process ecg signal taken mitbih databas use classifi differ arrhythmia togeth normal ecg sever publish method investig comparison best recognit rate select obtain afe classif perform achiev accuraci also suggest analyz present system addit white gaussian nois awgn environ db db conclud propos approach autom classif worth pursu larger sampl valid extend present studi

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