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. 2013 May 30;6(5):308-14.
doi: 10.4066/AMJ.2013.1640. Print 2013.

Reliable epileptic seizure detection using an improved wavelet neural network

Zarita Zainuddin  1 , Lai Kee Huong, Ong Pauline
Affiliations

Reliable epileptic seizure detection using an improved wavelet neural network

Zarita Zainuddin et al. Australas Med J. .

Abstract

Background: Electroencephalogram (EEG) signal analysis is indispensable in epilepsy diagnosis as it offers valuable insights for locating the abnormal distortions in the brain wave. However, visual interpretation of the massive amounts of EEG signals is time-consuming, and there is often inconsistent judgment between experts.

Aims: This study proposes a novel and reliable seizure detection system, where the statistical features extracted from the discrete wavelet transform are used in conjunction with an improved wavelet neural network (WNN) to identify the occurrence of seizures.

Method: Experimental simulations were carried out on a well-known publicly available dataset, which was kindly provided by the Epilepsy Center, University of Bonn, Germany. The normal and epileptic EEG signals were first pre-processed using the discrete wavelet transform. Subsequently, a set of statistical features was extracted to train a WNNs-based classifier.

Results: The study has two key findings. First, simulation results showed that the proposed improved WNNs-based classifier gave excellent predictive ability, where an overall classification accuracy of 98.87% was obtained. Second, by using the 10th and 90th percentiles of the absolute values of the wavelet coefficients, a better set of EEG features can be identified from the data, as the outliers are removed before any further downstream analysis.

Conclusion: The obtained high prediction accuracy demonstrated the feasibility of the proposed seizure detection scheme. It suggested the prospective implementation of the proposed method in developing a real time automated epileptic diagnostic system with fast and accurate response that could assist neurologists in the decision making process.

Keywords: Epileptic seizure detection; Kmeans clustering; fuzzy C-means clustering; type-2 fuzzy C-means clustering; wavelet neural network.

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Conflict of interest statement

CONFLICTS OF INTEREST

The authors declare that they have no competing interests

Figures

Figure 1
Figure 1. Block diagram for the proposed seizure detection scheme
Figure 2
Figure 2. Network architecture of wavelet neural networks
Figure 3
Figure 3. Clustering algorithm of T2FCM
See this image and copyright information in PMC

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