Explore real-time seizure detection using EEG in this comprehensive 53-minute lecture by Hyewon Jeong from Stanford University. Compare state-of-the-art models and signal feature extractors in a practical framework suitable for real-world applications. Learn about the general pipeline of seizure detection methods, deep neural networks for EEG seizure detection, and evaluation metrics including a newly proposed one for assessing practical aspects of detection models. Discover insights on optimal sliding window lengths, feature transformers, and signal feature extractors. Examine the performance of various approaches on raw data and for different seizure types. Gain valuable knowledge on EEG lead information, the TUH EEG dataset, and the challenges of implementing seizure detection in real-time scenarios.