Identification of Epileptic Spasms (ESES) Phases Using EEG Signals: A Vision Transformer Approach

TL;DR

This paper presents a novel Vision Transformer-based method for detecting Epileptic Spasms from EEG signals, achieving high accuracy and better generalization than traditional approaches, suitable for real-time clinical use.

Contribution

The study introduces a ViT model that processes frequency-domain EEG representations, improving ESES detection accuracy and efficiency over conventional methods.

Findings

Achieved 97% detection accuracy.

Effective in multi-channel EEG analysis.

Suitable for real-time clinical applications.

Abstract

This work introduces a new approach to the Epileptic Spasms (ESES) detection based on the EEG signals using Vision Transformers (ViT). Classic ESES detection approaches have usually been performed with manual processing or conventional algorithms, suffering from poor sample sizes, single-channel-based analyses, and low generalization abilities. In contrast, the proposed ViT model overcomes these limitations by using the attention mechanism to focus on the important features in multi-channel EEG data, which is contributing to both better accuracy and efficiency. The model processes frequency-domain representations of EEG signals, such as spectrograms, as image data to capture long-range dependencies and complex patterns in the signal. The model demonstrates high performance with an accuracy of 97% without requiring intensive data preprocessing, thus rendering it suitable for real-time clinical applications on a large scale. The method represents a significant development in the advancement of neurological disorders such as ESES in detection and analysis.