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Early seizure detection by applying frequency-based algorithm derived from the principal component analysis

Title
Early seizure detection by applying frequency-based algorithm derived from the principal component analysis
Authors
Lee J.Park J.Yang S.Kim H.Choi Y.S.Kim H.J.Lee H.W.Lee B.-U.
Ewha Authors
이병욱이향운
SCOPUS Author ID
이병욱scopus; 이향운scopus
Issue Date
2017
Journal Title
Frontiers in Neuroinformatics
ISSN
1662-5196JCR Link
Citation
vol. 11
Keywords
Early seizure detectionElectroencephalographyFrequency-based featurePrincipal component analysisSeizure onset
Publisher
Frontiers Media S.A.
Indexed
SCIE; SCOPUS WOS scopus
Abstract
The use of automatic electrical stimulation in response to early seizure detection has been introduced as a new treatment for intractable epilepsy. For the effective application of this method as a successful treatment, improving the accuracy of the early seizure detection is crucial. In this paper, we proposed the application of a frequency-based algorithm derived from principal component analysis (PCA), and demonstrated improved efficacy for early seizure detection in a pilocarpine-induced epilepsy rat model. A total of 100 ictal electroencephalographs (EEG) during spontaneous recurrent seizures from 11 epileptic rats were finally included for the analysis. PCA was applied to the covariance matrix of a conventional EEG frequency band signal. Two PCA results were compared: one from the initial segment of seizures (5 sec of seizure onset) and the other from the whole segment of seizures. In order to compare the accuracy, we obtained the specific threshold satisfying the target performance from the training set, and compared the False Positive (FP), False Negative (FN), and Latency (Lat) of the PCA based feature derived from the initial segment of seizures to the other six features in the testing set. The PCA based feature derived from the initial segment of seizures performed significantly better than other features with a 1.40%FP, zero FN, and 0.14 s Lat. These results demonstrated that the proposed frequency-based feature from PCA that captures the characteristics of the initial phase of seizure was effective for early detection of seizures. Experiments with rat ictal EEGs showed an improved early seizure detection rate with PCA applied to the covariance of the initial 5 s segment of visual seizure onset instead of using the whole seizure segment or other conventional frequency bands. © 2017 Lee, Park, Yang, Kim, Choi, Kim, Lee and Lee.
DOI
10.3389/fninf.2017.00052
Appears in Collections:
엘텍공과대학 > 전자공학과 > Journal papers
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