Reliable epileptic seizure detection using an improved wavelet neural network

Zainuddin, Zarita and Lai, Kee Huong and Ong , Pauline (2013) Reliable epileptic seizure detection using an improved wavelet neural network. Australasian Medical Journal, 6 (5). pp. 308-314. ISSN 18361935

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Official URL: https://www.ncbi.nlm.nih.gov/pubmed/23745153

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.

Item Type:Article
Uncontrolled Keywords:Epileptic seizure detection; fuzzy C-means clustering; K-means clustering; type-2 fuzzy C-means clustering; wavelet neural network
Subjects:Q Science > QA Mathematics > QA76 Computer software
Divisions:Faculty of Mechanical and Manufacturing Engineering > Department of Engineering Mechanics
ID Code:10754
Deposited By:Mr. Mohammad Shaifulrip Ithnin
Deposited On:20 Feb 2019 16:13
Last Modified:20 Feb 2019 16:13

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