Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting
This work addresses the need for standardized benchmarking in seizure detection for clinical applications, though it is incremental as it focuses on comparison rather than introducing a new method.
The authors tackled the problem of inconsistent evaluation in real-time seizure detection from EEG signals by conducting the first comprehensive comparison of state-of-the-art models and feature extractors under a realistic, real-time framework, proposing a new metric for practical assessment.
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. Therefore 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. Our code is available at https://github.com/AITRICS/EEG_real_time_seizure_detection.