Scholarlink

Accurate identification of coma etiology is essential for effective treatment in neurocritical care. However, significant challenges persist, especially in settings with limited resources. This study investigates the potential of electroencephalogram (EEG) signals from distinct brain regions for predicting coma etiology using an adapted Convolutional Neural Network combined with Long Short-Term Memory (CNN-LSTM) model. Building on prior hybrid CNN-LSTM architectures that processed all EEG channels collectively, we refined the model to integrate raw EEG data from specific regions (frontal, central, parietal, temporal, and occipital) with patient-specific clinical features (age, gender) and statistical EEG metrics. The model was evaluated on a real-world dataset of 50 patients with coma etiologies including traumatic brain injury, metabolic coma, and stroke, collected at the Clinical Hospital of the Federal University of Uberlândia (HC-UFU). Experimental results demonstrate that the frontal region, when combined with clinical features, yielded the highest predictive performance, achieving an accuracy of 70% and a F1 score (macro) of 68%. These findings highlight the frontal region’s critical role in capturing diagnostic patterns and underscore the value of integrating clinical data to enhance model robustness. By leveraging region-specific EEG analysis, this approach advances EEG-based predictive modeling. It offers a scalable and non-invasive tool to improve diagnostic precision in neurocritical care, particularly in underserved settings, and paves the way for future multimodal diagnostic strategies.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Coma Etiology Prediction Using a CNN-LSTM Model Guided by EEG Brain Regions

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • João Ludovico Maximiano Barbosa,
  • Murillo Guimarães Carneiro,
  • Sérgio Baldo-Júnior,
  • Renato Tinós,
  • Liang Zhao,
  • João Batista Destro-Filho

摘要

Accurate identification of coma etiology is essential for effective treatment in neurocritical care. However, significant challenges persist, especially in settings with limited resources. This study investigates the potential of electroencephalogram (EEG) signals from distinct brain regions for predicting coma etiology using an adapted Convolutional Neural Network combined with Long Short-Term Memory (CNN-LSTM) model. Building on prior hybrid CNN-LSTM architectures that processed all EEG channels collectively, we refined the model to integrate raw EEG data from specific regions (frontal, central, parietal, temporal, and occipital) with patient-specific clinical features (age, gender) and statistical EEG metrics. The model was evaluated on a real-world dataset of 50 patients with coma etiologies including traumatic brain injury, metabolic coma, and stroke, collected at the Clinical Hospital of the Federal University of Uberlândia (HC-UFU). Experimental results demonstrate that the frontal region, when combined with clinical features, yielded the highest predictive performance, achieving an accuracy of 70% and a F1 score (macro) of 68%. These findings highlight the frontal region’s critical role in capturing diagnostic patterns and underscore the value of integrating clinical data to enhance model robustness. By leveraging region-specific EEG analysis, this approach advances EEG-based predictive modeling. It offers a scalable and non-invasive tool to improve diagnostic precision in neurocritical care, particularly in underserved settings, and paves the way for future multimodal diagnostic strategies.