Seizure prediction based on improved vision transformer model for EEG channel optimization

Comput Methods Biomech Biomed Engin. 2024 Mar 6:1-12. doi: 10.1080/10255842.2024.2326097. Online ahead of print.ABSTRACTEpileptic seizures are unpredictable events caused by abnormal discharges of a patient's brain cells. Extensive research has been conducted to develop seizure prediction algorithms based on long-term continuous electroencephalogram (EEG) signals. This paper describes a patient-specific seizure prediction method that can serve as a basis for the design of lightweight, wearable and effective seizure-prediction devices. We aim to achieve two objectives using this method. The first aim is to extract robust feature representations from multichannel EEG signals, and the second aim is to reduce the number of channels used for prediction by selecting an optimal set of channels from multichannel EEG signals while ensuring good prediction performance. We design a seizure-prediction algorithm based on a vision transformer (ViT) model. The algorithm selects channels that play a key role in seizure prediction from 22 channels of EEG signals. First, we perform a time-frequency analysis of processed time-series signals to obtain EEG spectrograms. We then segment the spectrograms of multiple channels into many non-overlapping patches of the same size, which are input into the channel selection layer of the proposed model, named Sel-JPM-ViT, enabling it to select channels. Application of the Sel-JPM-ViT model to the Boston Children's Hospital-Massachusetts Institute of Techn...

Source: Computer Methods in Biomechanics and Biomedical Engineering - March 7, 2024 Category: Biomedical Engineering Authors: Nan Qi Yan Piao Hao Zhang Qi Wang Yue Wang Source Type: research