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J Neurosci Methods. 2016 Jan 15;257:45-54. doi: 10.1016/j.jneumeth.2015.08.026. Epub 2015 Aug 31.

Discriminating preictal and interictal brain states in intracranial EEG by sample entropy and extreme learning machine.

Author information

1
Computer Laboratory, University of Cambridge, Cambridge, United Kingdom. Electronic address: syd918@gmail.com.
2
School of Psychology, Cardiff University, United Kingdom.

Abstract

BACKGROUND:

Epilepsy is one of the most common neurological disorders approximately one in every 100 people worldwide are suffering from it. Uncontrolled epilepsy poses a significant burden to society due to associated healthcare cost to treat and control the unpredictable and spontaneous occurrence of seizures. The objective of this research is to develop and present a novel classification framework that is utilised to discriminate interictal and preictal brain activities via quantitative analysis of electroencephalogram (EEG) recordings.

NEW METHOD:

Sample entropy-based features were extracted in parallel from 6 intracranial EEG channels, and then these features were fed to the extreme learning machine model for classification. Performance was evaluated on the basis of sensitivity and specificity and validation was performed using stratified cross-validation approach.

RESULTS:

The proposed method can correctly distinguish interictal and preictal EEGs with a sensitivity of 86.75% and a specificity of 83.80%, on average, across 21 patients and 6 epileptic seizure origins.

COMPARISON WITH EXISTING METHOD:

Compared with traditional variance-based feature extraction, the proposed SampEn-based feature extraction method not only shows a significant improvement in the accuracy, but also has higher classification robustness and stability in terms of the much lower standard errors of classification accuracies across different evaluation periods. In addition, the proposed classification framework runs around 20 times faster than the support vector machine model during testing.

CONCLUSIONS:

The high accuracy and efficiency of the proposed method makes it feasible to extend it to the development of a real-time EEG-based brain monitoring system for epileptic seizure prediction.

KEYWORDS:

Electroencephalograms (EEG); Epileptic seizure prediction; Extreme learning machine; Feature extraction; Sample entropy

PMID:
26335801
DOI:
10.1016/j.jneumeth.2015.08.026
[Indexed for MEDLINE]

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