Epileptic Seizure Detection Using a Recurrent Neural Network With Temporal Features Derived From a Scale Mixture EEG Model

• Published in: IEEE access Vol. 12; pp. 162814 - 162824
• Main Authors: Furui, Akira, Onishi, Ryota, Akiyama, Tomoyuki, Tsuji, Toshio
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ablation; Accuracy; Amplitudes; Automation; Brain modeling; Convulsions & seizures; Covariance matrices; Diagnosis; Electroencephalogram (EEG); Electroencephalography; Epilepsy; epileptic seizure detection; Feature extraction; Frequencies; Hidden Markov models; Mixture models; Mixtures; Muscles; Neural networks; non-Gaussianity; recurrent neural network; Recurrent neural networks; scale mixture model; Seizures; stochastic model; Stochastic models; Stochastic processes
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3487637

Automated detection of epileptic seizures from scalp Electroencephalogram (EEG) is crucial for improving epilepsy diagnosis and management. This paper presents an automated inter-patient epileptic seizure detection method using multichannel EEG signals. The proposed method uses a scale mixture-based stochastic EEG model for feature extraction and a recurrent neural network for seizure detection. Specifically, the stochastic model, which accounts for uncertainties in EEG amplitude, is fitted to a specific frequency band to extract relevant seizure features. Then, a recurrent neural network-based recognition architecture learns the temporal evolution of these features. We evaluated our method using EEG data from 20 patients with focal epilepsy and conducted comprehensive assessments, including ablation studies on classifiers and features. Our results demonstrate that our approach outperforms static classifiers and existing feature sets, achieving high sensitivity while maintaining acceptable specificity. Furthermore, our feature set showed efficacy both independently and as a complement to existing features, indicating its robustness in seizure detection tasks. These findings reveal that learning the temporal evolution of the stochastic fluctuation and amplitude information of EEG extracted using a stochastic model enables highly accurate seizure detection, potentially advancing automated epilepsy diagnosis in clinical settings.