Epilepsy is a neurological disorder that causes recurrent seizures and sometimes loss of awareness. Around 30% of epileptic patients continue to have seizures despite taking anti-seizure medication. The ability to predict the future occurrence of seizures would enable the patients to take precautions against probable injuries and administer timely treatment to abort or control impending seizures. In this study, we introduce a Transformer-based approach called Multi-channel Vision Transformer (MViT) for automated and simultaneous learning of the spatio-temporal-spectral features in multi-channel EEG data. Continuous wavelet transform, a simple yet efficient pre-processing approach, is first used for turning the time-series EEG signals into image-like time-frequency representations named Scalograms. Each scalogram is split into a sequence of fixed-size non-overlapping patches, which are then fed as inputs to the MViT for EEG classification. Extensive experiments on three benchmark EEG datasets demonstrate the superiority of the proposed MViT algorithm over the state-of-the-art seizure prediction methods, achieving an average prediction sensitivity of 99.80% for surface EEG and 90.28–91.15% for invasive EEG data.

Recent studies have shown that wavelet transform can effectively be used for noise reduction in the context of partial discharge (PD) signal detection and classification. Several thresholding approaches for wavelet denoising have been reported in the literature. In this study, a novel wavelet threshold estimation method, named energy conservation‐based thresholding (ECBT), is introduced. The proposed thresholding function is capable of conserving a significant portion of the original signal energy, while the threshold value is determined based on the relative difference between the original and noisy signal energies. The proposed method is first applied to PD signals contaminated with different levels of simulated noise. Results show that ECBT produces a denoised PD signal with higher signal‐to‐noise ratio (SNR) and less distortion than PDs produced by the existing wavelet methods. Then, ECBT is modified to address actual PD signals corrupted with real noise, where a robust SNR estimation method is derived to estimate the noise level embedded in the measured PD signals. The denoised PD signals indicate that the proposed method yields higher reduction in noise levels than other methods.