Machine and Deep Learning-Based Seizure Prediction: A Scoping Review on the Use of Temporal and Spectral Features

Epileptic seizures result from abnormal brain activity, posing significant health risks due to their sudden and unpredictable nature. Accurate seizure prediction is crucial for improving patient outcomes and enabling timely interventions. Recent advancements in artificial intelligence (AI), particularly in machine learning (ML) and deep learning (DL), have significantly enhanced seizure detection and prediction. This review provides a comprehensive overview of seizure prediction models that integrate temporal and spectral features as inputs or enhanced representations for ML and DL models. Emphasizing convolutional neural networks (CNNs) and other deep architectures, we explore the role of time-domain and frequency-domain features, such as wavelet transforms, short-time Fourier transforms, and spectrogram representations, in improving model performance. Additionally, the review discusses common challenges, including feature interpretability, generalizability across datasets, and computational efficiency. By highlighting recent advancements and limitations, this study provides insights into optimizing spectral and temporal feature integration for seizure prediction, paving the way for more robust and clinically viable AI-based solutions.