SincVAE: A new semi-supervised approach to improve anomaly detection on EEG data using SincNet and variational autoencoder

Over the past few decades, electroencephalography monitoring has become a pivotal tool for diagnosing neurological disorders, particularly for detecting seizures. Epilepsy, one of the most prevalent neurological diseases worldwide, affects approximately 1 % of the population. These patients face significant risks, underscoring the need for reliable, continuous seizure monitoring in daily life. Most of the techniques discussed in the literature rely on supervised machine learning methods. However, the challenge of accurately labeling variations in epileptic electroencephalography waveforms complicates the use of these approaches. Additionally, the rarity of ictal events introduces a high imbalance within the data, which could lead to poor prediction performance in supervised learning approaches. Instead, a semi-supervised approach allows training the model only on data that does not contain seizures, thus avoiding the issues related to the data imbalance. This work introduces a semi-supervised approach for detecting epileptic seizures from electroencephalography data based on a novel deep learning-based method called SincVAE. This method integrates SincNet, designed to learn an ad-hoc array of bandpass filters, as the first layer of a variational autoencoder, potentially eliminating the preprocessing stage where informative frequency bands are identified and isolated. Experimental evaluations on the Bonn and CHB-MIT datasets indicate that SincVAE improves seizure detection in electroencephalography data, with the capability to identify early seizures during the preictal stage and monitor patients throughout the postictal stage. • Epilepsy affects approximately the 1 % of the population. • Supervised machine learning methods face challenges with imbalanced seizure data. • SincVAE is a semi-supervised method that requires only non-seizure data for training. • SincVAE allows preictal and postictal stages monitoring.