基于脑电图的小鼠超急性大血管闭塞性卒中检测深度学习模型

IF 5 1区 医学 Q1 NEUROSCIENCES
Tan Zhang, Xiaolin Li, Xinxin Hu, Zhiyong Zhou, Qingchun Mu, Xiaoke Chai, Qing Lan, Jizong Zhao
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引用次数: 0

摘要

目的建立基于脑电数据的深度学习模型,用于超急性大血管闭塞(LVO)脑卒中的早期准确检测。方法采用pMCAO小鼠模型模拟LVO脑卒中,采集超急性期高分辨率脑电数据。采用专门的深度学习架构EEGNet,建立了基于脑电图信号的超急性LVO卒中检测模型。进行了七次交叉验证,以评估模型在多个指标上的性能,包括准确性、AUC、精度、召回率和F1分数。结果该模型的总体准确率为97.9%,AUC为0.977,在超急性期表现出良好的诊断效果。脑卒中检测在发病后1.5小时内是可靠的,在以小时划分的所有五个时间间隔内,分类准确率超过95%。t-SNE分析证实了特征提取的有效性,并与假手术小鼠进行比较,验证了该模型对脑卒中相关EEG变化的特异性。结论基于脑电图的深度学习模型在超急性LVO脑卒中检测中具有鲁棒性,具有较高的准确性和特异性。这些结果突出了它作为早期中风诊断的生物标志物的潜力,以及作为临床和院前环境中实时、无创监测的基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

EEG-Based Deep Learning Model for Hyper-Acute Large Vessel Occlusion Stroke Detection in Mice

EEG-Based Deep Learning Model for Hyper-Acute Large Vessel Occlusion Stroke Detection in Mice

Objective

This study aims to develop a deep learning model for the early and accurate detection of hyper-acute large vessel occlusion (LVO) stroke using EEG data.

Methods

A pMCAO mouse model was used to simulate LVO stroke, with high-resolution EEG data collected during the hyper-acute phase. EEGNet, a specialized deep learning architecture, was employed to develop a model based on EEG signals for the detection of hyper-acute LVO strokes. Seven-fold cross-validation was conducted to evaluate the model's performance across multiple metrics, including accuracy, AUC, precision, recall, and F1 score.

Results

The model achieved an overall accuracy of 97.9% and an AUC of 0.977, demonstrating excellent diagnostic performance across the hyper-acute phase. Stroke detection was reliable within 1.5 h post-onset, with classification accuracies exceeding 95% in all five time intervals segmented by hour. t-SNE analysis confirmed effective feature extraction, and comparisons with sham-operated mice validated the model's specificity for stroke-related EEG changes.

Conclusion

The EEG-based deep learning model showed robust performance in hyper-acute LVO stroke detection, achieving high accuracy and specificity. These results highlight its potential as a biomarker for early stroke diagnosis and as a foundation for real-time, non-invasive monitoring in clinical and prehospital settings.

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来源期刊
CNS Neuroscience & Therapeutics
CNS Neuroscience & Therapeutics 医学-神经科学
CiteScore
7.30
自引率
12.70%
发文量
240
审稿时长
2 months
期刊介绍: CNS Neuroscience & Therapeutics provides a medium for rapid publication of original clinical, experimental, and translational research papers, timely reviews and reports of novel findings of therapeutic relevance to the central nervous system, as well as papers related to clinical pharmacology, drug development and novel methodologies for drug evaluation. The journal focuses on neurological and psychiatric diseases such as stroke, Parkinson’s disease, Alzheimer’s disease, depression, schizophrenia, epilepsy, and drug abuse.
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