基于MSC-BiLSTM融合fMRI时空特征信息的癫痫发作检测  

Seizure Detection Based on MSC-BiLSTM Integrating fMRI Spatial and Temporal Characteristics

作　　者：杜欣霖 许开航 陈均霞 蒋思思 罗程[2] 龚津南 DU Xinlin;XU Kaihang;CHEN Junxia;JIANG Sisi;LUO Cheng;GONG Jinnan(School of Computer Science,Chengdu University of Information Technology,Chengdu 610225,China;School of Life Science and Technology,University of Electronic Science and Technology of China,Chengdu 611731,China)

机构地区：[1]成都信息工程大学计算机学院,四川成都610225 [2]电子科技大学生命科学与技术学院,四川成都611731

出　　处：《软件导刊》2025年第3期23-30,共8页Software Guide

基　　金：国家自然科学基金项目(62003058);四川省自然科学基金项目(2021YJ0165)。

摘　　要：癫痫是一种常见的中枢神经系统性脑部疾病,头皮脑电EEG是诊断癫痫的金标准。然而,癫痫放电的产生机制、诱发因素以及放电后大脑获得恢复的机制仍未完全明确。为此,提出一种将多尺度卷积与双线性长短期记忆网络相结合的模型MSC-BiLSTM,自动提取功能磁共振成像fMRI中的有效信息,在全面捕捉时空和时间动力学特征的同时解析放电前诱发和放电后恢复过程中大脑获得介观尺度的时空特征。实验结果表明,该模型在EEG-fMRI同步采集数据集上的癫痫检测准确率达到99.1%。通过分析模型的可视化结果,研究了癫痫发作期与未发作期大脑中7个主要脑网络的相位差异原因。研究结果为癫痫发作的自动检测提供了思路。Epilepsy is a common central nervous system brain disease,and scalp EEG is the gold standard for diagnosing epilepsy.However,the mechanism of epileptic discharge,triggering factors,and the mechanism of brain recovery after discharge are still not fully understood.Therefore,a model MSC BiLSTM combining multi-scale convolution with bilinear long short-term memory network is proposed to automatically extract effective information from functional magnetic resonance imaging(fMRI),while comprehensively capturing spatiotemporal and temporal dynamic features,and analyzing the spatiotemporal features obtained by the brain during pre discharge induction and post discharge recovery processes at the mesoscale.The experimental results show that the model achieves an accuracy of 99.1%in epilepsy detection on the EEG fMRI synchronous acquisition dataset.By analyzing the visualization results of the model,the reasons for the phase differences of seven major brain networks in the epileptic seizure and non seizure periods were studied.The research results provide ideas for automatic detection of epileptic seizures.

关 键 词：头皮脑电 功能磁共振成像 多尺度卷积 BiLSTM 癫痫检测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]