Lima Eloise de Oliveira, Souza Neto José Maurício Ramos de, Castro Felipe Leonardo Seixas, Silva Letícia Maria, Laurentino Rebeca Andrade, Calado Vitória Ferreira, Torquato Isolda Maria Barros, Moreira Karen Lúcia de Araújo Freitas, Andrade Suellen Marinho
Aging and Neuroscience Laboratory (LABEN), Federal University of Paraíba, João Pessoa, PB, Brazil.
Center for Alternative and Renewable Energies (CEAR), Federal University of Paraíba, João Pessoa, PB, Brazil.
Brain Topogr. 2025 Jan 22;38(2):23. doi: 10.1007/s10548-024-01093-9.
Electroencephalography microstates (EEG-MS) show promise to be a neurobiological biomarker in stroke. Thus, the aim of the study was to identify biomarkers to discriminate stroke patients from healthy individuals based on EEG-MS and clinical features using a machine learning approach. Fifty-four participants (27 stroke patients and 27 healthy age and sex-matched controls) were recruited. We recorded EEG-MS using 32 channels during eyes-closed and eyes-open conditions and analyzed the four classical EEG-MS maps (A, B, C, D). Clinical information and motor aspects were evaluated. A machine learning method using k-means algorithms to discriminate stroke patients from healthy subjects showed that the most influential parameters in clustering were balance scores and microstate parameters (duration and coverage of microstate A, duration, coverage and occurrence of microstates C and global variance explained). To evaluate the quality of clustering, the Silhouette score was applied and the score was close to 0.20, indicating that the clusters overlap. These results are encouraging and support the usefulness of these methods for classifying stroke patients in order to contribute to the development of therapeutic strategies, improve the clinical management of these patients, and consequently reduce the associated costs.
脑电图微状态（EEG-MS）有望成为中风的一种神经生物学生物标志物。因此，本研究的目的是使用机器学习方法，基于EEG-MS和临床特征识别区分中风患者与健康个体的生物标志物。招募了54名参与者（27名中风患者和27名年龄、性别匹配的健康对照）。我们在闭眼和睁眼条件下使用32个通道记录EEG-MS，并分析四种经典的EEG-MS图谱（A、B、C、D）。评估了临床信息和运动方面。一种使用k均值算法区分中风患者与健康受试者的机器学习方法表明，聚类中最具影响力的参数是平衡分数和微状态参数（微状态A的持续时间和覆盖率、微状态C的持续时间、覆盖率和发生率以及全局方差解释率）。为了评估聚类质量，应用了轮廓系数，该系数接近0.20，表明聚类存在重叠。这些结果令人鼓舞，并支持这些方法在对中风患者进行分类方面的有用性，以便为治疗策略的制定做出贡献，改善这些患者的临床管理，并因此降低相关成本。
