软件导刊2025，Vol.24Issue(3)：23-30,8.DOI:10.11907/rjdk.241114
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
摘要
Abstract
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 automatical-ly extract effective information from functional magnetic resonance imaging(fMRI),while comprehensively capturing spatiotemporal and tem-poral dynamic features,and analyzing the spatiotemporal features obtained by the brain during pre discharge induction and post discharge re-covery 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Key words
electroencephalography/functional magnetic resonance imaging/multi-scale convolution/BiLSTM/epilepsy detection分类
计算机与自动化引用本文复制引用
杜欣霖,许开航,陈均霞,蒋思思,罗程,龚津南..基于MSC-BiLSTM融合fMRI时空特征信息的癫痫发作检测[J].软件导刊,2025,24(3):23-30,8.基金项目
国家自然科学基金项目(62003058) （62003058）
四川省自然科学基金项目(2021YJ0165) （2021YJ0165）
