Key result
Wavelet-based neural network classifies normal and partial epilepsy ECG beats with up to ~98% accuracy.
Wavelet-based neural networks can detect electrocardiographic changes in patients with partial epilepsy with high accuracy (up to 97.50%).
May inform ECG monitoring in epilepsy; leaves open rigorous validation before clinical use.
In this study a wavelet-based neural network model, employing the multilayer perceptron, is presented for the detection of electrocardiographic changes in patients with partial epilepsy. Decision making is performed in two stages: feature extraction using the wavelet transform, and multilayer perceptron neural networks (MLPNNs) trained with the backpropagation, delta-bar-delta, extended delta-bar-delta and quick propagation algorithms as classifiers. The classification results, the values of statistical parameters and performance evaluation parameters of the MLPNNs trained with different algorithms are compared. Two types of electrocardiogram beats (normal and partial epilepsy) obtained from the MIT-BIH database were classified with accuracy varying from 90.00% to 97.50% by the MLPNNs trained with different algorithms.
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Güler et al. (2005) studied Partial epilepsy. Wavelet-based neural network model (MLPNNs) vs. Different training algorithms was evaluated on Classification accuracy of normal and partial epilepsy ECG beats. A wavelet-based neural network model classified normal and partial epilepsy electrocardiogram beats with an accuracy varying from 90.00% to 97.50%.
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