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Content Provider | IEEE Xplore Digital Library |
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Author | Tong Zhang Fusheng Yang Qingyu Tang |
Copyright Year | 1999 |
Description | Author affiliation: Dept. of Electr. Eng., Tsinghua Univ., Beijing, China (Tong Zhang) |
Abstract | The automatic detection and classification of epileptiform waves in EEG has great clinical significance. A hierarchical approach for achieving this task is presented in this paper which is based on time-scale analysis of the EEG. With 16-channel input EEG data, a preliminary screening is first done to pick out segments suspicious of containing epileptic waves. It is based on an adaptive filtering process to separate the non-stationary transients from the stationary background EEG. These segments are then decomposed into different scales by applying the continuous wavelet transform. After feature extraction, recognition and classification are conducted based on the combination of a fuzzy neural network and rule-based expert system. Finally, a statistical analysis of the obtained results is made and reported. The initial clinical results are encouraging. |
File Size | 87998 |
File Format | |
ISBN | 0780356748 |
ISSN | 1094687X |
DOI | 10.1109/IEMBS.1999.804097 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 1999-10-13 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Epilepsy Electroencephalography Continuous wavelet transforms Wavelet transforms Feature extraction Fuzzy neural networks Wavelet domain Artificial neural networks Adaptive filters Expert systems |
Content Type | Text |
Resource Type | Article |
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