An Analysis Research for Digitized Features of Epileptic EEG Using SVM

Xin Xu; Bin Lv; Jie Song; Wei Xiang Shi; Yan Ting Hu; Shan Cheng Yan

doi:10.4028/www.scientific.net/AMM.239-240.1169

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The Improvement of Katsevich Reconstruction Algorithm Based on Cone-Beam CT
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Find Point Correspondences on Contour between Template Image and its Target with SVD and Euclidean Distance
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Frame Reconstruction with Missing Data from Multiple Images
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Removing Blinks in Video-Oculography
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An Analysis Research for Digitized Features of Epileptic EEG Using SVM
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Study on Filtering Algorithm of Image Using Matlab
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Design and Implementation of SoPC based Low power Asynchronous Image Processor
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Radar Target Tracking Error Research Using Kalman Filter-Based Algorithms
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Carrier Tracking Performance Comparison of Standards and the Square-Root Kalman Filter
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 239-240An Analysis Research for Digitized Features of...

An Analysis Research for Digitized Features of Epileptic EEG Using SVM

Abstract:

Epilepsy is one of the most common neurological disorders that greatly disturb patients’ daily lives. Traditional epileptic diagnosis relies on tedious visual screening by neurologists from lengthy EEG recording that requires the presence of seizure (ictal) activities. We proposed to study automated epileptic diagnosis using interictal EEG data that was much easier to collect than ictal data. The research aims to develop an automated diagnostic system that can use interictal EEG data to diagnose whether the person is epileptic. This system could also test epileptic seizures in order to provide doctors with further tests and potential monitor of patients. To test such a system, we extract power spectral feature, Petrosian fractal dimension, Higuchi fractal dimension and Hjorth parameters for analysis, from which we find our system can be used in patient monitoring(seizure detection) and seizure focus localization, with 98.333% and75.5% accuracy respectively.

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Periodical:

Applied Mechanics and Materials (Volumes 239-240)

Pages:

1169-1172

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.239-240.1169

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Online since:

December 2012

Authors:

Xin Xu, Bin Lv, Jie Song, Wei Xiang Shi, Yan Ting Hu, Shan Cheng Yan

Keywords:

Electroencephalography (EEG), Epilepsy, Probabilistic Neural Network (PNN), Signal Processing

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