Key result
ResNet-short+LSTM outperforms benchmark architectures for real-time raw EEG seizure detection with 0.92 AUROC.
Why the study?
Existing deep learning models for seizure detection in EEG signals have not been fairly compared due to distinct experimental settings and lack of real-time training for on-device applications.
This study provides a standardized framework for comparing deep learning models for real-time EEG seizure detection, addressing the lack of fair comparisons in previous literature.
May aid real-time EEG monitoring in epilepsy; leaves open prospective validation before clinical adoption.
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.
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Lee et al. (2022) studied Epileptic seizures (n=642). ResNet-short+LSTM and CNN2D+LSTM models vs. Other benchmark deep learning models (e.g., ChronoNet, ResNet18, MobileNetV3) was evaluated on Area Under the Receiver Operating Characteristic curve (AUROC) for real-time seizure detection. The ResNet-short+LSTM model achieved the highest performance for real-time seizure detection on raw EEG signals with an AUROC of 0.92, outperforming other benchmark architectures.
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