The causes of mental disorders are complex, and early recognition and early intervention are recognized as effective way to avoid irreversible brain damage over time. The existing computer-aided recognition methods mostly focus on multimodal data fusion, ignoring the asynchronous acquisition problem of multimodal data. For this reason, this paper proposes a framework of mental disorder recognition based on visibility graph (VG) to solve the problem of asynchronous data acquisition. First, time series electroencephalograms (EEG) data are mapped to spatial visibility graph. Then, an improved auto regressive model is used to accurately calculate the temporal EEG data features, and reasonably select the spatial metric features by analyzing the spatiotemporal mapping relationship. Finally, on the basis of spatiotemporal information complementarity, different contribution coefficients are assigned to each spatiotemporal feature and to explore the maximum potential of feature so as to make decisions. The results of controlled experiments show that the method in this paper can effectively improve the recognition accuracy of mental disorders. Taking Alzheimer's disease and depression as examples, the highest recognition rates are 93.73% and 90.35%, respectively. In summary, the results of this paper provide an effective computer-aided tool for rapid clinical diagnosis of mental disorders.
精神障碍疾病成因复杂，早识别早干预是公认避免随时间推移造成大脑不可逆转损伤的有效途径。已有的计算机辅助识别方法多关注于多模态数据融合，忽略了多模态数据异步采集问题。为此，本文提出一种基于可视图的精神障碍识别框架，以期解决数据异步采集问题。首先，通过映射时序脑电（EEG）数据到空间可视图（VG）；然后，采用改进自回归模型，精准计算时序EEG数据特征，分析时空映射关系，合理选择空间度量特征；最后，以时空信息互补为基础，为各时空特征赋予不同贡献系数，发掘特征最大潜能并做出决策。对照实验结果表明，本文方法能够有效提高精神障碍疾病的识别准确率，以阿尔茨海默症与抑郁症为例，分别获得了最高93.73%和90.35%的识别率。综上所述，本文结果为精神障碍疾病的快速临床诊断提供了一种有效的计算机辅助工具。.
Keywords: Electroencephalograms; Information complementarity; Mental disorder recognition; Visibility graph.