摘要
Epilepsy is a long-term neurological condition marked by recurrent seizures,which result from abnormal electrical activity in the brain that disrupts its normal functioning.Traditional methods for detecting epilepsy through machine learning typically utilize discrete-time models,which inadequately represent the continuous dynamics of electroencephalogram(EEG)signals.To overcome this limitation,we introduce an innovative approach that employs Neural Ordinary Differential Equations(NODEs)to model EEG signals as continuous-time systems.This allows for effective management of irregular sampling and intricate temporal patterns.In contrast to conventional techniques,such as Convolutional Neural Networks(CNNs)and Recurrent Neural Networks(RNNs),which necessitate fixedlength inputs and often struggle with long-term dependencies,our framework incorporates:(1)a NODE block to capture continuous-time EEG dynamics,(2)a feature extraction module tailored for seizure-specific patterns,and(3)an attention-based fusion mechanism to enhance interpretability in classification.When evaluated on three publicly accessible EEG datasets,including those from Boston Children’s Hospital and the Massachusetts Institute of Technology(CHB-MIT)and the Temple University Hospital(TUH)EEG Corpus,the model demonstrated an average accuracy of 98.2%,a sensitivity of 97.8%,a specificity of 98.3%,and an F1-score of 97.9%.Additionally,the inference latency was reduced by approximately 30%compared to standard CNN and Long Short-Term Memory(LSTM)architectures,making it well-suited for real-time applications.The method’s resilience to noise and its adaptability to irregular sampling enhance its potential for clinical use in real-time settings.
基金
extend their appreciation to the King Salman Center for Disability Research for funding this work through Research Group No.KSRG-2024-223.
