Epileptic Seizure Classification with Symmetric and Hybrid Bilinear Models

2020/01/15 by Tennison Liu, Nhan Duy Truong, Liu, Tennison +7 · 5 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Mathematics · Medicine · Neuroscience · Psychology · #Artificial intelligence #Bilinear interpolation #Computer science #Computer vision #Convolutional neural network #EEG and Brain-Computer Interfaces #Epilepsy #Epilepsy research and treatment #Epilepsy surgery #Epileptic seizure #FOS: Computer and information sciences #FOS: Electrical engineering #Feature (linguistics) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Bioinformatics #Machine learning #Neuroscience #Pattern recognition (psychology) #Pooling #Psychology #Signal Processing (eess.SP) #Stereoelectroencephalography #cs.LG #eess.SP #electronic engineering #information engineering #stat.ML

paper · pdf · doi:10.48550/arxiv.2001.06282

published in arXiv (Cornell University) (Cornell University) · 9 pages, 4 figures, 3 tables

arxiv created 2020/01/15 · openalex publication_date 2020/01/15 · arxiv updated 2020/01/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

Epilepsy affects nearly 1% of the global population, of which two thirds can be treated by anti-epileptic drugs and a much lower percentage by surgery. Diagnostic procedures for epilepsy and monitoring are highly specialized and labour-intensive. The accuracy of the diagnosis is also complicated by overlapping medical symptoms, varying levels of experience and inter-observer variability among clinical professions. This paper proposes a novel hybrid bilinear deep learning network with an application in the clinical procedures of epilepsy classification diagnosis, where the use of surface electroencephalogram (sEEG) and audiovisual monitoring is standard practice. Hybrid bilinear models based on two types of feature extractors, namely Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are trained using Short-Time Fourier Transform (STFT) of one-second sEEG. In the proposed hybrid models, CNNs extract spatio-temporal patterns, while RNNs focus on the characteristics of temporal dynamics in relatively longer intervals given the same input data. Second-order features, based on interactions between these spatio-temporal features are further explored by bilinear pooling and used for epilepsy classification. Our proposed methods obtain an F1-score of 97.4% on the Temple University Hospital Seizure Corpus and 97.2% on the EPILEPSIAE dataset, comparing favourably to existing benchmarks for sEEG-based seizure type classification. The open-source implementation of this study is available at https://github.com/NeuroSyd/Epileptic-Seizure-Classification

Citations

• Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
• Operational classification of seizure types by the International League Against Epilepsy: Position Paper of the ILAE Commission for Classification and Terminology
• Deep learning with convolutional neural networks for EEG decoding and visualization

Cited by

• DistilCLIP-EEG: Enhancing Epileptic Seizure Detection Through Multi-modal Learning and Knowledge Distillation

Related

• A Robust Deep Learning Approach for Automatic Classification of Seizures Against Non-seizures
• An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach
• Residual Deep Convolutional Neural Network for EEG Signal Classification in Epilepsy
• SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification
• From Epilepsy Seizures Classification to Detection: A Deep Learning-based Approach for Raw EEG Signals