Unsupervised Deep Embedding for Robust Epileptic Seizure Detection

Epilepsy is a neurological disorder characterized by recurrent, unpredictable seizures, posing significant risks to morbidity and mortality. Despite numerous advancements in automated seizure detection methods, their clinical application remains limited due to several challenges. One primary issue is the prolonged training phase required by supervised models. Additionally, many existing techniques struggle to generalize effectively across diverse patient populations. To address these limitations, we propose a novel methodology, the Deep Variational Gaussian Mixture (DVGM) model. This approach integrates a deep variational autoencoder (VAE) to embed input EEG data, followed by Singular Value Decomposition (SVD) for dimensionality reduction and enhancement of representational quality. Subsequently, a Gaussian Mixture Model (GMM) is employed for clustering. Unlike supervised machine learning (ML) and deep learning (DL) methods that require extensive training, the DVGM utilizes deep clustering (DC) algorithms, enabling efficient and effective seizure detection. The DVGM model was trained on the publicly available Children's Hospital of Boston (CHB) dataset and tested on a French dataset acquired at the Centre Hospitalier Universitaire (CHU) of Angers. The results demonstrated outstanding performance. This highlights its ability to overcome generalization challenges.Clinical relevance- This study introduces the DVGM model, which enhances the accuracy and efficiency of epileptic seizure detection. By addressing challenges such as prolonged training times and poor generalizability across patient populations, this methodology offers a scalable and reliable solution for analyzing large-scale EEG data. Its demonstrated success across diverse datasets suggesting its potential for improving diagnostic workflows and patient outcomes in clinical neurology.