Journal
EPILEPSY & BEHAVIOR
Volume 52, Issue -, Pages 187-193Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.yebeh.2015.07.043
Keywords
S-transform; Singular value decomposition (SVD); Seizure detection; Bayesian linear discriminant analysis (BLDA)
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Funding
- Natural Science Foundation of Shandong Province [ZR2013FZ002]
- Program of Science and Technology of Suzhou [ZXY2013030]
- Development Program of Science and Technology of Shandong [201 4GSF118171]
- Fundamental Research Funds of Shandong University [2014QY008]
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Automatic seizure detection plays a significant role in the diagnosis of epilepsy. This paper presents a novel method based on S-transform and singular value decomposition (SVD) for seizure detection. Primarily, S-transform is performed on EEG signals, and the obtained time-frequency matrix is divided into submatrices. Then, the singular values of each submatrix are extracted using singular value decomposition (SVD). Effective features are constructed by adding the largest singular values in the same frequency band together and fed into Bayesian linear discriminant analysis (BLDA) classifier for decision. Finally, postprocessing is applied to obtain higher sensitivity and lower false detection rate. A total of 183.07 hours of intracranial EEG recordings containing 82 seizure events from 20 patients were used to evaluate the system. The proposed method had a sensitivity of 96.40% and a specificity of 99.01%, with a false detection rate of 0.16/ h. (C) 2015 Elsevier Inc. All rights reserved.
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