Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:2025-2029. doi: 10.1109/EMBC46164.2021.9629976.
Electroencephalogram (EEG) is a widely used technique to diagnose psychological disorders. Until now, most of the studies focused on the diagnosis of a particular psychological disorder using EEG. We propose a generic approach to diagnose the different type of psychological disorders with high accuracy. The proposed approach is tested on five different datasets and three psychological disorders. Electrodes having higher signal to noise ratio are selected from the raw EEG signals. Multiple linear and non-linear features are then extracted from the selected electrodes. After feature selection, machine learning is used to diagnose the psychological disorders. We kept the same generic approach for all the datasets and diseases and achieved 93%, 85% and 80% F1 score on Schizophrenia, Epilepsy and Parkinson disease, respectively.
脑电图（EEG）是一种广泛用于诊断心理障碍的技术。到目前为止，大多数研究都集中在使用 EEG 来诊断特定的心理障碍。我们提出了一种通用的方法，可以高精度地诊断不同类型的心理障碍。该方法在五个不同的数据集和三种心理障碍上进行了测试。从原始 EEG 信号中选择具有更高信噪比的电极。然后从选定的电极中提取多个线性和非线性特征。在特征选择之后，使用机器学习来诊断心理障碍。我们对所有数据集和疾病都采用相同的通用方法，并在精神分裂症、癫痫和帕金森病上分别获得了 93%、85%和 80%的 F1 分数。
