Real-Time Seizure Detection Using EEG: A Comprehensive Comparison of Recent Approaches Under a Realistic Setting
Abstract
Electroencephalogram (EEG) is an important diagnostic test that physicians use to record brain activity and detect seizures by monitoring the signals. There have been several attempts to detect seizures and abnormalities in EEG signals with modern deep learning models to reduce the clinical burden. However, they cannot be fairly compared against each other as they were tested in distinct experimental settings. Also, some of them are not trained in real-time seizure detection tasks, making it hard for on-device applications. In this work, for the first time, we extensively compare multiple state-of-the-art models and signal feature extractors in a real-time seizure detection framework suitable for real-world application, using various evaluation metrics including a new one we propose to evaluate more practical aspects of seizure detection models.
Cite
Text
Lee et al. "Real-Time Seizure Detection Using EEG: A Comprehensive Comparison of Recent Approaches Under a Realistic Setting." Proceedings of the Conference on Health, Inference, and Learning, 2022.Markdown
[Lee et al. "Real-Time Seizure Detection Using EEG: A Comprehensive Comparison of Recent Approaches Under a Realistic Setting." Proceedings of the Conference on Health, Inference, and Learning, 2022.](https://mlanthology.org/chil/2022/lee2022chil-realtime/)BibTeX
@inproceedings{lee2022chil-realtime,
title = {{Real-Time Seizure Detection Using EEG: A Comprehensive Comparison of Recent Approaches Under a Realistic Setting}},
author = {Lee, Kwanhyung and Jeong, Hyewon and Kim, Seyun and Yang, Donghwa and Kang, Hoon-Chul and Choi, Edward},
booktitle = {Proceedings of the Conference on Health, Inference, and Learning},
year = {2022},
pages = {311-337},
volume = {174},
url = {https://mlanthology.org/chil/2022/lee2022chil-realtime/}
}