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Content Provider | IEEE Xplore Digital Library |
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Author | Ling Han Hong Wang Cong Liu Chunsheng Li |
Copyright Year | 2012 |
Description | Author affiliation: School of Electrical Engineering, Shenyang University of Technology Shenyang, Liaoning Province, China (Chunsheng Li) || School of Mechanical Engineering and Automation, Northeastern University, Shenyang, Liaoning Province, China (Hong Wang; Cong Liu) || Sino-Dutch Biomedical and Information, Engineering School, Northeastern University, Shenyang, Liaoning Province, China (Ling Han) |
Abstract | Electroencephalogram is the recording of brain electrical activity and it contains valuable information related to the different physiological states of the brain. In this study, we present a new approach to detect epileptic seizure. The new scheme was based on discrete wavelet transform and sample entropy analysis of EEG signals. Decision making is performed in two stages: feature extraction by computing the wavelet coefficients and the sample entropy and detection by using support vector machine. The analysis results depicted that during seizure activity EEG had lower sample entropy values compared to normal EEG. This suggested that epileptic EEG was more predictable or less complex than the normal EEG. |
Starting Page | 759 |
Ending Page | 762 |
File Size | 516317 |
Page Count | 4 |
File Format | |
ISBN | 9781467322386 |
e-ISBN | 9781467322379 |
e-ISBN | 9781467322362 |
DOI | 10.1109/ICInfA.2012.6246920 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2012-06-06 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Support vector machines Wavelet transform Sample entropy Feature extraction Electroencephalography Entropy Discrete wavelet transforms Support vector machine electroencephalogram (EEG) |
Content Type | Text |
Resource Type | Article |
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