Deep Learning for scalp High Frequency Oscillations Identification

TL;DR

This paper explores the use of deep learning, specifically CNNs, to automatically detect scalp high frequency oscillations in EEG signals, aiming to improve epilepsy diagnostics.

Contribution

It demonstrates that a CNN can effectively identify scalp HFOs from EEG data, enabling end-to-end automated detection in challenging scalp recordings.

Findings

Deep learning achieves competitive detection performance.

CNN-based approach simplifies preprocessing and feature extraction.

Scalp HFO detection is feasible with deep neural networks.

Abstract

Since last 2 decades, High Frequency Oscillations (HFOs) are studied as a promising biomarker to localize the epileptogenic zone of patients with refractory focal epilepsy. As HFOs visual detection is time consuming and subjective, automatization of HFO detection is required. Most HFO detectors were developed on invasive electroencephalograms (iEEG) whereas scalp electroencephalograms (EEG) are used in clinical routine. In order HFO detection can benefit to more patients, scalp HFO detectors has to be developed. However, HFOs identification in scalp EEG is more challenging than in iEEG since scalp HFOs are of lower rate, lower amplitude and more likely to be corrupted by several sources of artifacts than iEEG HFOs. The main goal of this study is to explore the ability of deep learning architecture to identify scalp HFOs from the remaining EEG signal. Hence, a binary classification Convolutional Neural Network (CNN) is learned to analyze High Density Electroencephalograms (HD-EEG). EEG signals are first mapped into a 2D time-frequency image, several color definitions are then used as an input for the CNN. Experimental results show that deep learning allows simple end-to-end learning of preprocessing, feature extraction and classification modules while reaching competitive performance.