A Moth–Flame Optimized Echo State Network and Triplet Feature Extractor for Epilepsy Electro-Encephalography Signals

Xue-song Tang, Luchao Jiang, Kuangrong Hao (), Tong Wang and Xiaoyan Liu
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Xue-song Tang: Faculty of Information Science, Donghua University, Shanghai 201620, China
Luchao Jiang: Faculty of Information Science, Donghua University, Shanghai 201620, China
Kuangrong Hao: Faculty of Information Science, Donghua University, Shanghai 201620, China
Tong Wang: Faculty of Information Science, Donghua University, Shanghai 201620, China
Xiaoyan Liu: Faculty of Information Science, Donghua University, Shanghai 201620, China

Mathematics, 2023, vol. 11, issue 6, 1-16

Abstract: The analysis of epilepsy electro-encephalography (EEG) signals is of great significance for the diagnosis of epilepsy, which is one of the common neurological diseases of all age groups. With the developments of machine learning, many data-driven models have achieved great performance in EEG signals classification. However, it is difficult to select appropriate hyperparameters for the models to file a specific task. In this paper, an evolutionary algorithm enhanced model is proposed, which optimizes the fixed weights of the reservoir layer of the echo state network (ESN) according to the specific task. As evaluating a feature extractor relies heavily on the classifiers, a new feature distribution evaluation function (FDEF) using the label information of EEG signals is defined as the fitness function, which is an objective way to evaluate the performance of a feature extractor that not only focuses on the degree of dispersion, but also considers the relation amongst triplets. The performance of the proposed method is verified on the Bonn University dataset with an accuracy of 98.16% and on the CHB-MIT dataset with the highest sensitivity of 96.14%. The proposed method outperforms the previous EEG methods, as it can automatically optimize the hyperparameters of ESN to adjust the structure and initial parameters for a specific classification task. Furthermore, the optimization direction by using FDEF as the fitness of MFO no longer relies on the performance of the classifier but on the relative separability amongst classes.

Keywords: epilepsy detection; moth–flame optimization; echo state network; feature extraction; EEG signals (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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