Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:6053-6057. doi: 10.1109/EMBC46164.2021.9630549.
A valid evaluation of neurological functions after stroke may improve clinical decision-making. The aim of this study was to compare the performance of EEG-related indexes in differentiating stroke patients from control participants, and to investigate pathological EEG changes after chronic stroke. 20 stroke and 13 healthy participants were recruited, and spontaneous EEG signals were recorded during the resting state. EEG rhythms and complexity were calculated based on Fast Fourier Transform and the fuzzy approximate entropy (fApEn) algorithm. The results showed a significant difference of alpha rhythm (p = 0.019) and fApEn (p = 0.003) of EEG signals from brain area among ipsilesional, contralesion hemisphere of stroke patients and corresponding brain hemisphere of healthy participants. EEG fApEn had the best classification accuracy (84.85%), sensitivity (85.00%), and specificity (84.62%) among these EEG-related indexes. Our study provides a potential method to evaluate alterations in the properties of the injured brain, which help us to understand neurological change in chronic strokes.
对中风后神经功能进行有效的评估可以改善临床决策。本研究旨在比较 EEG 相关指标在区分中风患者和对照组方面的性能，并研究慢性中风后的病理性 EEG 变化。共招募了 20 名中风患者和 13 名健康参与者，并在静息状态下记录自发 EEG 信号。基于快速傅里叶变换和模糊近似熵（fApEn）算法计算 EEG 节律和复杂度。结果显示，中风患者对侧和同侧大脑半球以及健康参与者相应大脑半球的 EEG 信号的 alpha 节律（p = 0.019）和 fApEn（p = 0.003）存在显著差异。在这些 EEG 相关指标中，EEG fApEn 的分类准确性（84.85%）、敏感度（85.00%）和特异性（84.62%）最高。本研究为评估受损大脑特性的改变提供了一种潜在的方法，有助于我们了解慢性中风中的神经变化。
