Raeisi Zahra, Bashiri Omid, EskandariNasab MohammadReza, Arshadi Mahdi, Golkarieh Alireza, Najafzadeh Hossein
Department of Computer Science, University of Fairleigh Dickinson, Vancouver Campus, Vancouver, Canada.
Department of Kinesiology and Nutrition Sciences, University of Nevada, Las Vegas, NV 89154 USA.
Cogn Neurodyn. 2025 Dec;19(1):68. doi: 10.1007/s11571-025-10251-z. Epub 2025 May 3.
Schizophrenia remains a challenging neuropsychiatric disorder with complex diagnostic processes. Current clinical approaches often rely on subjective assessments, highlighting the critical need for objective, quantitative diagnostic methods. This study aimed to develop a robust classification approach for schizophrenia using EEG microstate analysis and advanced machine learning techniques. We analyzed EEG signals from 14 healthy individuals and 14 patients with schizophrenia during a 15-min resting-state session across 19 EEG channels. A data augmentation strategy expanded the dataset to 56 subjects in each group. The signals were preprocessed and segmented into five frequency bands (delta, theta, alpha, beta, gamma) and five microstates (A, B, C, D, E) using k-means clustering. Five key features were extracted from each microstate: duration, occurrence, standard deviation, coverage, and frequency. A Deep Neural Network (DNN) model, along with other machine learning classifiers, was developed to classify the data. A comprehensive fivefold cross-validation approach evaluated model performance across various EEG channels, frequency bands, and feature combinations. Significant alterations in microstate transition probabilities were observed, particularly in higher frequency bands. The gamma band showed the most pronounced differences, with a notable disruption in D → A transitions (absolute difference = 0.100). The Random Forest classifier achieved the highest accuracy of 99.94% ± 0.12%, utilizing theta band features from the F8 frontal channel. The deep neural network model demonstrated robust performance with 98.31% ± 0.68% accuracy, primarily in the occipital region. Feature size 2 consistently provided optimal classification across most models. Our study introduces a novel, high-precision EEG microstate analysis approach for schizophrenia diagnosis, offering an objective diagnostic tool with potential applications in neuropsychiatric disorders. The findings reveal critical insights into neural dynamics associated with schizophrenia, demonstrating the potential for transforming clinical diagnostic practices through advanced machine learning and neurophysiological feature extraction.
精神分裂症仍然是一种具有挑战性的神经精神疾病，其诊断过程复杂。当前的临床方法通常依赖主观评估，这凸显了对客观、定量诊断方法的迫切需求。本研究旨在利用脑电图微状态分析和先进的机器学习技术，开发一种用于精神分裂症的强大分类方法。我们分析了14名健康个体和14名精神分裂症患者在15分钟静息状态下通过19个脑电图通道采集的脑电图信号。一种数据增强策略将每组数据集扩展到56名受试者。信号经过预处理，并使用k均值聚类分割为五个频段（δ、θ、α、β、γ）和五个微状态（A、B、C、D、E）。从每个微状态中提取了五个关键特征：持续时间、出现次数、标准差、覆盖率和频率。开发了一个深度神经网络（DNN）模型以及其他机器学习分类器来对数据进行分类。一种全面的五折交叉验证方法评估了模型在各种脑电图通道、频段和特征组合下的性能。观察到微状态转换概率有显著变化，特别是在高频段。γ频段显示出最明显的差异，D→A转换存在明显中断（绝对差异=0.100）。随机森林分类器利用F8额部通道的θ频段特征，实现了99.94%±0.12%的最高准确率。深度神经网络模型表现出强大的性能，准确率为98.31%±0.68%，主要在枕叶区域。特征大小2在大多数模型中始终提供最佳分类。我们的研究引入了一种用于精神分裂症诊断的新颖、高精度脑电图微状态分析方法，提供了一种客观的诊断工具，在神经精神疾病中具有潜在应用。研究结果揭示了与精神分裂症相关的神经动力学的关键见解，证明了通过先进的机器学习和神经生理特征提取改变临床诊断实践的潜力。
