ArticleNPJ digital medicine2020
Weak supervision as an efficient approach for automated seizure detection in electroencephalography.
Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Human-artificial intelligence collaboration for comanaging epilepsy in real time.Nature medicine · 2026Article
- From Computational Models to Clinical Decision-Making in Epilepsy: Bridging the Gap Between Theory and Practice.Brain topography · 2026Review
- Patient-independent automated pediatric seizure monitoring based on expert-labeled electrographic ictal data as core reference knowledge.Clinical and experimental pediatrics · 2026Article
- A meta-learning method with reduced data requirements for training and updating deep learning models on wearable devices.Communications medicine · 2026Article
- Artificial Intelligence and Machine Learning in Pediatric Epilepsy: A Systematic Review.Neurology and therapy · 2026Review
- EEG-based harmful brain activity classification using deep learning and feature fusion.Scientific reports · 2026Article
- PSD-LW-DCN: a generalizable power spectral density based lightweight deep convolutional neural network for seizure detection.Scientific reports · 2026Article
- An Implantable Device that Converses with Patients and Learns to Co-Manage Epilepsy.medRxiv : the preprint server for health sciences · 2026Article
- Bayesian Uncertainty-aware Deep Learning with noisy labels: Tackling annotation ambiguity in EEG seizure detection.PloS one · 2026Article
- Population-based spectral characteristics of normal interictal scalp EEG inform diagnosis and treatment planning in focal epilepsy.Scientific reports · 2025Article
- Canine EEG helps human: cross-species and cross-modality epileptic seizure detection via multi-space alignment.National science review · 2025Article
- The Value of Normal Interictal EEGs in Epilepsy Diagnosis and Treatment Planning: A Retrospective Cohort Study using Population-level Spectral Power and Connectivity Patterns.medRxiv : the preprint server for health sciences · 2025Article
- Using artificial intelligence to optimize anti-seizure treatment and EEG-guided decisions in severe brain injury.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025Review
- Dynamic GNNs for Precise Seizure Detection and Classification from EEG Data.Advances in knowledge discovery and data mining : ... Pacific-Asia Conference, PAKDD ..., proceedings. Pacific-Asia Conference on Knowledge Discovery and Data Mining · 2024Article
- Towards trustworthy seizure onset detection using workflow notes.NPJ digital medicine · 2024Article
- Time-Series Anomaly Detection Based on Dynamic Temporal Graph Convolutional Network for Epilepsy Diagnosis.Bioengineering (Basel, Switzerland) · 2024Article
- Article
- Classification of epileptic seizures in EEG data based on iterative gated graph convolution network.Frontiers in computational neuroscience · 2024Article
- Assisting schizophrenia diagnosis using clinical electroencephalography and interpretable graph neural networks: a real-world and cross-site study.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2023Article
- Estimating Patient-Level Uncertainty in Seizure Detection Using Group-Specific Out-of-Distribution Detection Technique.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
Automated seizure detection from electroencephalography (EEG) would improve the quality of patient care while reducing medical costs, but achieving reliably high performance across patients has proven difficult. Convolutional Neural Networks (CNNs) show promise in addressing this problem, but they are limited by a lack of large labeled training datasets. We propose using imperfect but plentiful archived annotations to train CNNs for automated, real-time EEG seizure detection across patients. While these weak annotations indicate possible seizures with precision scores as low as 0.37, they are commonly produced in large volumes within existing clinical workflows by a mixed group of technicians, fellows, students, and board-certified epileptologists. We find that CNNs trained using such weak annotations achieve Area Under the Receiver Operating Characteristic curve (AUROC) values of 0.93 and 0.94 for pediatric and adult seizure onset detection, respectively. Compared to currently deployed clinical software, our model provides a 31% increase (18 points) in F1-score for pediatric patients and a 17% increase (11 points) for adult patients. These results demonstrate that weak annotations, which are sustainably collected via existing clinical workflows, can be leveraged to produce clinically useful seizure detection models.
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