Seizure detection approach using S-transform and singular value decomposition

Abstract 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...

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Bibliographic Details
Published in:Epilepsy & behavior 2015-11, Vol.52 (Pt A), p.187-193
Main Authors: Xia, Yudan, Zhou, Weidong, Li, Chengcheng, Yuan, Qi, Geng, Shujuan
Format: Article
Language:English
Subjects:
Algorithms
Bayes Theorem
Bayesian linear discriminant analysis (BLDA)
Data Interpretation, Statistical
Discriminant Analysis
Electrocorticography - statistics & numerical data
Epilepsy - diagnosis
False Positive Reactions
Humans
Likelihood Functions
Neurology
Reproducibility of Results
S-transform
Seizure detection
Seizures - diagnosis
Singular value decomposition (SVD)
Software
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Summary:Abstract 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.
ISSN:1525-5050
1525-5069
DOI:10.1016/j.yebeh.2015.07.043