Departamento de Psiquiatría, Hospital Virgen de la Luz, 16002 Cuenca, Spain.
Departamento de Sistema Informáticos, Universidad de Castilla-La Mancha, 02071 Albacete, Spain.
Schizophr Res. 2023 Nov;261:36-46. doi: 10.1016/j.schres.2023.09.010. Epub 2023 Sep 8.
Electroencephalography is a method of detecting and analyzing electrical activity in the brain. This electrical activity can be recorded and processed to aid in the clinical diagnosis of mental disorders. In this study, a novel system for classifying schizophrenia patients from EEG recordings is presented. The developed algorithm decomposes the EEG signals into a system of radial basis functions using the method of fuzzy means. This decomposition helps to obtain the information from the various electrodes of the EEG and allows separating between healthy controls and patients with schizophrenia. The proposed method has been compared with classical machine learning algorithms, such as, K-Nearest Neighbor, Adaboost, Support Vector Machine, and Bayesian Linear Discriminant Analysis. The results show that the proposed method obtains the highest values in terms of balanced accuracy, recall, precision and F1 score, close to 93 % in all cases. The model developed in this study can be implemented in brain activity analysis systems that help in the prediction of patients with schizophrenia.
脑电图是一种检测和分析大脑电活动的方法。这种电活动可以被记录和处理，以帮助临床诊断精神障碍。在这项研究中，提出了一种从 EEG 记录中分类精神分裂症患者的新系统。所开发的算法使用模糊均值方法将 EEG 信号分解为径向基函数系统。这种分解有助于从 EEG 的各个电极获取信息，并允许区分健康对照者和精神分裂症患者。所提出的方法已与经典机器学习算法（例如 K-最近邻、Adaboost、支持向量机和贝叶斯线性判别分析）进行了比较。结果表明，在所提出的方法中，平衡准确性、召回率、精度和 F1 评分均获得了最高值，在所有情况下接近 93%。本研究中开发的模型可以在大脑活动分析系统中实现，有助于预测精神分裂症患者。
