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Deep learning-based real-time seizure detection and multi-seizure classification on pediatric EEG
Hyewon Jeong1, Kwanhyung Lee2,3, Seyun Kim4
1Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States.
Insights
Deep learning models accurately detect and classify multiple pediatric seizure types in real-time using electroencephalography (EEG) data. This method offers a reliable approach for monitoring childhood epilepsy with high performance and speed.
Area of Science:
- Neurology
- Artificial Intelligence
- Biomedical Engineering
Background:
- Epilepsy is a common neurological disorder in children, necessitating accurate and timely detection methods.
- Current seizure detection methods may lack the accuracy and real-time capabilities required for effective pediatric epilepsy management.
Purpose of the Study:
- To develop and validate a deep learning-based system for real-time detection and classification of multiple seizure types in pediatric patients.
- To assess the performance of deep learning models in analyzing electroencephalography (EEG) data for clinical application.
Main Methods:
- Retrospective collection of EEG recordings from pediatric patients (3 months to 18 years) diagnosed with various epilepsy types.
- Downsampling of EEG data to 200 Hz for real-time processing and application of deep learning models (ResNet with Long-Short Term Network, ResNet50).
Main Results:
- The ResNet with Long-Short Term Network achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.98 and an Area Under the Precision-Recall Curve (APROC) of 0.73 for real-time seizure detection.
- ResNet50 demonstrated superior performance in multi-class seizure detection, with an AUROC of 0.99 and an Area Under the Precision-Recall Curve (APPRC) of 0.99.
Conclusions:
- The proposed deep learning approach provides robust, real-time detection and classification of multiple seizure types in pediatric epilepsy.
- The system demonstrates effective application to real-world clinical EEG datasets, offering realistic performance and speed for monitoring childhood seizures.
Background And Objective:
To develop a reliable and accurate seizure detection method using deep learning models capable of detecting and classifying multiple seizure types in real time.
Methods:
We retrospectively collected electroencephalography (EEG) recordings, which were acquired as part of routine diagnostic tests for patients aged 3 months to ≤18 years of age with childhood absence epilepsy, infantile epileptic spasms syndrome, other generalized epilepsy, and focal epilepsy, between January 2018 and December 2022 at Severance Children's Hospital. We used EEG recordings from both seizure and non-seizure patients, which were downsampled to 200 Hz for real-time seizure detection and multi-classification.
Results:
Of the 199 patients (620 seizures), 49 (297 seizures) belonged to the childhood absence epilepsy group, 16 (200 seizures) to the infantile epileptic spasms syndrome group, 14 (76 seizures) to other generalized epilepsy group, 19 (47 seizures) to focal epilepsy group, and 101 to the normal group. The results showed the best overall performance of AUROC 0.98 and APROC of 0.73 with ResNet with Long-Short Term Network and a 12 s sliding window on real-time seizure detection task. Furthermore, ResNet50 without the frequency bands feature extractor showed the best overall weighted performance for multi-class seizure detection with 0.99 AUROC and 0.99 APPRC.
Discussion:
Our approach proposes robust methods which include EEG preprocessing strategy with real-time detection/classification of multiple seizures, which helps monitor pediatric seizure. The result shows that real-time seizure detection can be effectively applied to real-world clinical datasets from a pediatric epilepsy unit with realistic performance and speed.
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