Xinyu Liu
ABSTRACT
Automatic recognition of electroencephalogram (EEG) signals in epileptogenic zone is of great medical significance for computer-aided epilepsy surgery. In the analysis of EEG signals, in order to achieve the purpose of automatic recognition of EEG signals in epileptogenic zone, a method based on deep reinforcement learning is proposed in this paper. First, the local mean decomposition (LMD) algorithm is applied to decompose the original EEG signals. Statistical feature extraction is then performed. Finally, a deep reinforcement learning method, Deep Q-Network (DQN), is used to train the features and finally classify them. The accuracy, sensitivity and specificity of the experimental classification results were 89.28%, 89.88% and 88.68%, respectively. Compared with other traditional machine learning methods, this method shows good classification results.
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