Automatic recognition of epileptic EEG patterns via Extreme Learning Machine and multiresolution feature extraction

• Published in: Expert systems with applications Vol. 40; no. 14; pp. 5477 - 5489
• Main Authors: Song, Yuedong, Zhang, Jiaxiang
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 15.10.2013; Elsevier
• Subjects: Applied sciences; Artificial intelligence; Biological and medical sciences; Brain; Computer science; control theory; systems; Connectionism. Neural networks; Data processing. List processing. Character string processing; Diagnosis; EEC; Electrodiagnosis. Electric activity recording; Electroencephalogram (EEG); Entropy; Epilepsy; Epilepsy diagnosis; Exact sciences and technology; Extreme Learning Machine (ELM); Feature extraction; Feature recognition; Frequency bands; Genetic algorithm (GA); Genetic algorithms; Headache. Facial pains. Syncopes. Epilepsia. Intracranial hypertension. Brain oedema. Cerebral palsy; Investigative techniques, diagnostic techniques (general aspects); Medical sciences; Memory organisation. Data processing; Multiresolution analysis; Nervous system; Nervous system (semeiology, syndromes); Neurology; Polyethylenes; Software; Genetic algorithm (GA); Feature extraction; Electroencephalogram (EEG); Multiresolution analysis; Epilepsy diagnosis; Extreme Learning Machine (ELM); Scuffing; Epilepsy; Alteration; Electroencephalography; Entropy; Modeling; Parabolic equation; Feedforward neural nets; Frequency band; Efficiency; Selection criterion; Diagnosis; Neurological disorder; Learning algorithm; Pattern extraction; Permutation; Pattern recognition; Neural network; Wavelet transformation; Genetic algorithm; Mental disorder; Automatic recognition
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2013.04.025

•We propose a novel feature extraction method based on multiresolution complexity analysis.•Nonlinear parameters of certain EEG frequency bands represent characteristics of epileptic EEGs.•A wavelet-Extreme Learning Machine framework is proposed to recognize epileptic patterns in EEG.•High recognition accuracy is achieved by genetic algorithm based feature selection. Epilepsy is one of the most common neurological disorders- approximately one in every 100 people worldwide are suffering from it. In this paper, a novel pattern recognition model is presented for automatic epilepsy diagnosis. Wavelet transform is investigated to decompose EEG into five EEG frequency bands which approximate to delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ) bands. Complexity based features such as permutation entropy (PE), sample entropy (SampEn), and the Hurst exponent (HE) are extracted from both the original EEG signals and each of the frequency bands. The wavelet-based methodology separates the alterations in PE, SampEn, and HE in specific frequency bands of the EEG. The effectiveness of these complexity based measures in discriminating between normal brain state and brain state during the absence of seizures is evaluated using the Extreme Learning Machine (ELM). It is discovered that although there exists no significant differences in the feature values extracted from the original EEG signals, differences can be recognized when the features are examined within specific EEG frequency bands. A genetic algorithm (GA) is developed to choose feature subsets that are effective for enhancing the recognition performance. The GA is also examined for weight alteration for both sensitivity and specificity. The results show that the abnormal EEG diagnosis rate of the model without the involvement of the genetic algorithm is 85.9%. However, the diagnosis rate of the model increases to 94.2% when the genetic algorithm is integrated as a feature selector.