Neural Networks
Comparison of extrasystolic ECG signal classifiers using discrete wavelet transforms
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Pattern Recognition Letters
Atrial fibrillation classification with artificial neural networks
Pattern Recognition
An expert system for speaker identification using adaptive wavelet sure entropy
Expert Systems with Applications: An International Journal
A fuzzy clustering neural network architecture for classification of ECG arrhythmias
Computers in Biology and Medicine
A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification
Expert Systems with Applications: An International Journal
Automated ECG diagnostic P-wave analysis using wavelets
Computer Methods and Programs in Biomedicine
A neural network with asymmetric basis functions for feature extraction of ECG P waves
IEEE Transactions on Neural Networks
Computers and Electrical Engineering
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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.