Comprehensive Survey of Machine Learning Techniques for Seizure Detection and Prediction: Challenges and Future Directions

Epilepsy is a neurological disorder affecting around 50 million individuals globally, with seizures posing significant detriments to their quality of life. Enhancing the accuracy of seizure detection and forecasting through technological means can lead to transformative improvements in patient management and outcomes. This survey offers a detailed examination of machine-learning techniques for automated seizure recognition and anticipation using electroencephalogram (EEG) data. This paper examine various approaches including traditional machine learning models, deep learning architectures like convolutional and recurrent neural networks, and hybrid methods. Key challenges include EEG signal complexity, inter-patient variability, and limited labeled data. We analyze performance metrics, datasets, and clinical translation potential across studies. We confront key challenges such as the intricacy of EEG signals, the variability among patients, and the scarcity of annotated data. The survey evaluates performance indicators, available datasets, and the prospect of clinical implementation across diverse studies. Although deep learning approaches exhibit substantial potential, hurdles pertaining to adaptability and elucidation persist. Future directions, including the incorporation of multimodal data, the application of federated learning strategies, and the pursuit of explainable AI, are poised to propel the domain forward. This survey endeavors to reconcile technological advancements with clinical needs, offering a vital compendium for both researchers and healthcare practitioners focused on the cutting-edge of machine learning in epilepsy care.