An EEG Analysis Research For Epileptics Using Probabilistic Neural Network

Abstract:

Nowadays, diagnosis for epilepsy depends on many systems helping the neurologists to quickly find interesting segments from the lengthy signal by automatic seizure detection. However, we notice that it is very difficult, to obtain long-term EEG data with seizure activities for epilepsy patients in areas lack of medical resources and trained neurologists. Therefore, we propose to study automated epileptic diagnosis using interictal EEG data that is much easier to collect than ictal data. The research, therefore, aims to develop an automated diagnostic system that can use interictal EEG data to diagnose whether the person is epileptic. To develop such a system, we extract from the EEG data three classes of features which respectively are Petrosian fractal dimension, Higuchi fractal dimension and Hjorth parameters and build a Probabilistic Neural Network (PNN) fed with these features. Meanwhile, we also broach demand for data standardization by analysis with EEG of epileptic patients.

• Periodical: Advanced Materials Research (Volumes 605-607)
• Pages: 2270-2273
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.605-607.2270
• Online since: December 2012
• Authors: Xin Xu, Jie Song, Yan Ting Hu, Wei Xiang Shi, Xu Zhu
• Keywords: Electroencephalography (EEG), Epilepsy, Probabilistic Neural Network (PNN), Signal Processing

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