Support vector machines for seizure detection in an animal model of chronic epilepsy

We compare the performance of three support vector machine (SVM) types: weighted SVM, one-class SVM and support vector data description (SVDD) for the application of seizure detection in an animal model of chronic epilepsy. Large EEG datasets (273 h and 91 h respectively, with a sampling rate of 1 k...

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Bibliographic Details
Published in:Journal of neural engineering 2010-06, Vol.7 (3), p.036001-036001
Main Authors: Nandan, Manu, Talathi, Sachin S, Myers, Stephen, Ditto, William L, Khargonekar, Pramod P, Carney, Paul R
Format: Article
Language:English
Subjects:
Algorithms
Animals
Artificial Intelligence
Brain - physiopathology
Chronic Disease
Diagnosis, Computer-Assisted - methods
Electroencephalography - methods
Epilepsy - diagnosis
Epilepsy - physiopathology
Humans
Pattern Recognition, Automated - methods
Rats
Rats, Sprague-Dawley
Reproducibility of Results
Sensitivity and Specificity
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Summary:We compare the performance of three support vector machine (SVM) types: weighted SVM, one-class SVM and support vector data description (SVDD) for the application of seizure detection in an animal model of chronic epilepsy. Large EEG datasets (273 h and 91 h respectively, with a sampling rate of 1 kHz) from two groups of rats with chronic epilepsy were used in this study. For each of these EEG datasets, we extracted three energy-based seizure detection features: mean energy, mean curve length and wavelet energy. Using these features we performed twofold cross-validation to obtain the performance statistics: sensitivity (S), specificity (K) and detection latency (tau) as a function of control parameters for the given SVM. Optimal control parameters for each SVM type that produced the best seizure detection statistics were then identified using two independent strategies. Performance of each SVM type is ranked based on the overall seizure detection performance through an optimality index metric (O). We found that SVDD not only performed better than the other SVM types in terms of highest value of the mean optimality index metric (O⁻) but also gave a more reliable performance across the two EEG datasets.
ISSN:1741-2552
1741-2560
1741-2552
DOI:10.1088/1741-2560/7/3/036001