Tong Wei, Zhang Jingxin, Chen Fangni, Shi Wei, Zhang Lei, Wan Jian
School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, China.
The Zhejiang Key Laboratory of Biomedical Intelligent Computing Technology, Hangzhou, 310023, China.
Sci Rep. 2025 Apr 24;15(1):14287. doi: 10.1038/s41598-025-92807-x.
Stroke is the leading cause of disability and death worldwide. It severely affects patients' quality of life and imposes a huge burden on the society in general. The diagnosis of stroke relies predominantly on the use of neuroimaging. The identification of stroke using electroencephalogram (EEG) in the clinical assessment of stroke has been underutilized. An EEG feature fusion based light gradient-boosting machine (LightGBM) model was proposed to achieve a fast diagnosis of non-stroke, ischemic stroke, and hemorrhagic stroke. This study aims to capture the essential difference between non-stroke, ischemic stroke, and hemorrhagic stroke. An optimal fusion feature set originated from approximate entropy and fuzzy entropy of EEG signal was constructed. To verify the effectiveness of the EEG fusion feature, the Tree-structured Parzen Estimator optimized LightGBM classifier (TPELGBM) was used for the classification. The ZJU4H EEG dataset used for analysis in this study was obtained from the Fourth Affiliated Hospital of Zhejiang University, China. The proposed ApFu-TPELGBM model exhibited excellent classification results, which achieved a precision of 0.9676, recall of 0.9669, and f1-score of 0.9672. To our knowledge, it was the most accurate classifier for EEG-based stroke diagnosis so far. The ApFu-TPELGBM model can determine the stroke type anywhere EEG signals can be collected, even before the patient is admitted to a hospital. Rapid and accurate diagnosis of stroke using EEG signals may become a promising approach in the clinical assessment of stroke.
中风是全球致残和致死的主要原因。它严重影响患者的生活质量，总体上给社会带来巨大负担。中风的诊断主要依赖于神经影像学检查。在中风的临床评估中，利用脑电图（EEG）识别中风的方法尚未得到充分利用。为实现对非中风、缺血性中风和出血性中风的快速诊断，提出了一种基于EEG特征融合的轻量级梯度提升机（LightGBM）模型。本研究旨在捕捉非中风、缺血性中风和出血性中风之间的本质差异。构建了一个源自EEG信号近似熵和模糊熵的最优融合特征集。为验证EEG融合特征的有效性，采用树结构帕曾估计器优化的LightGBM分类器（TPELGBM）进行分类。本研究用于分析的ZJU4H EEG数据集来自中国浙江大学医学院附属第四医院。所提出的ApFu-TPELGBM模型展现出优异的分类结果，其精确率为0.9676，召回率为0.9669，F1分数为0.9672。据我们所知，这是迄今为止基于EEG的中风诊断中最准确的分类器。ApFu-TPELGBM模型可以在任何能够采集EEG信号的地方确定中风类型，甚至在患者入院之前。利用EEG信号快速准确地诊断中风可能成为中风临床评估中一种很有前景的方法。
