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<p>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.</p>

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Eeg Microstates and Balance Parameters for Stroke Discrimination: A Machine Learning Approach

  • 期刊论文

  • 发表日期：

    2025年1月22日

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  • Eloise de Oliveira Lima,
  • José Maurício Ramos de Souza Neto,
  • Felipe Leonardo Seixas Castro,
  • Letícia Maria Silva,
  • Rebeca Andrade Laurentino,
  • Vitória Ferreira Calado,
  • Isolda Maria Barros Torquato,
  • Karen Lúcia de Araújo Freitas Moreira,
  • Suellen Marinho Andrade

摘要

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.