Epilepsy EEG classification and recognition algorithm based on PSO-CNN.

Chao Lv; Jintao Nian; Xu Yaru; Song Bo

doi:10.1117/12.2641275

4 August 2022 Epilepsy EEG classification and recognition algorithm based on PSO-CNN.

Chao Lv, Jintao Nian, Xu Yaru, Song Bo

Proceedings Volume 12306, Second International Conference on Digital Signal and Computer Communications (DSCC 2022); 123061F (2022) https://doi.org/10.1117/12.2641275
Event: Second International Conference on Digital Signal and Computer Communications (DSCC 2022), 2022, Changchun, China

Abstract

In the research of automatic classification of epilepsy EEG (Electroencephalogram), the detection model parameters are often set based on artificial experience, and the structure lacks adaptability. The epilepsy EEG signal is used as the research object. After preprocessing the EEG signal, Use CNN (convolutional neural network) for feature extraction, and use the PSO (Particle Swarm Optimization) algorithm to adaptively optimize the CNN model parameters to form a PSO-CNN epilepsy classification model. The algorithm proposed has an accuracy of 94.8% on the epilepsy dataset of the University of Bonn. Compared with traditional detection methods and other deep learning methods, the proposed algorithm achieves a higher accuracy.

Citation Download Citation

Chao Lv, Jintao Nian, Xu Yaru, and Song Bo "Epilepsy EEG classification and recognition algorithm based on PSO-CNN.", Proc. SPIE 12306, Second International Conference on Digital Signal and Computer Communications (DSCC 2022), 123061F (4 August 2022); https://doi.org/10.1117/12.2641275

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