Research on Recognition Method of Adolescent Schizophrenia Based on EEG Feature Fusion

Xiaoqin Lian, Zitong Wang, Mohao Cai,Chao Gao,Wenyang Guan,Yonggang Gong

2023 17th International Conference on Complex Medical Engineering (CME)（2023）

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Abstract

Schizophrenia is a common mental illness, and adolescent schizophrenia has a higher recurrence rate and disability rate than adult schizophrenia. In order to improve the recognition accuracy of adolescent schizophrenia, this study proposed a classification method of adolescent schizophrenia combined with artificial intelligence technology. In this method, the pre-processed original electroencephalogram (EEG) signals are decomposed by Discrete Wavelet Transform (DWT) and Ensemble Empirical Mode Decomposition (EEMD), respectively, and then the decomposed signals are extracted and the extracted features are fused. Finally, five machine learning models are used for classification. The results show that compared with DWT or EEMD methods alone, the feature fusion method proposed in this paper exhibits a higher accuracy in classification, in which the K-Nearest Neighbours (KNN) model achieves the best classification performance. The identification accuracy, recall, precision, F1 score, and Kappa coefficient of the model were 97.48 % , 95.71%, 99.56%, 97.59%, and 0.9494, respectively, which proved that the proposed method could effectively distinguish adolescent schizophrenia patients from healthy adolescents, and could assist doctors in disease analysis, providing a new method for the diagnosis of adolescent schizophrenia.

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Key words

Adolescent schizophrenia,electroencephalogram (EEG),Feature fusion,Machine learning

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【要点】：本研究提出了一种基于脑电图（EEG）特征融合的青少年精神分裂症识别方法，通过结合离散小波变换（DWT）和集成经验模态分解（EEMD）提高了分类准确性。

【方法】：采用DWT和EEMD对预处理后的EEG信号进行分解，提取并融合特征，再利用五种机器学习模型进行分类。

【实验】：使用未具体提及名称的数据集，实验结果显示，与单独使用DWT或EEMD相比，提出的特征融合方法在分类准确性上更高，其中K-最近邻（KNN）模型的性能最优，准确率、召回率、精确度、F1分数和Kappa系数分别为97.48%、95.71%、99.56%、97.59%和0.9494。