ArticleInternational journal of neural systems2020
Automated Detection of Interictal Epileptiform Discharges from Scalp Electroencephalograms by Convolutional Neural Networks.
Article in International journal of neural systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Epilepsy-IEDs: An automated machine learning model for detecting interictal epileptiform discharges from scalp electroencephalograms.iScience · 2026Article
- Conditional spatial classification of expert-confirmed interictal epileptiform discharge epochs: An EEG-ECG ablation and SHAP analysis.PloS one · 2026Article
- Digital Response Test in Epilepsy assesses interictal epileptiform discharge effects in real time.Epilepsia · 2026Article
- Reduction of interictal epileptic burden by pulsatile corticoid therapy in children with drug-resistant epilepsy-How stable is the effect?Epileptic disorders : international epilepsy journal with videotape · 2025Article
- Automated detection of interictal epileptiform discharges with few electroencephalographic channels.Epilepsia · 2025Article
- Electromagnetic Source Imaging in Presurgical Evaluation of Children with Drug-Resistant Epilepsy.Journal of visualized experiments : JoVE · 2024Article
- Pulsatile corticoid therapy reduces interictal epileptic activity burden in children with genetic drug-resistant epilepsy.Epilepsia open · 2024Article
- Differentiating Epileptic and Psychogenic Non-Epileptic Seizures Using Machine Learning Analysis of EEG Plot Images.Sensors (Basel, Switzerland) · 2024Article
- Passive and active markers of cortical excitability in epilepsy.Epilepsia · 2023Review
- A Two-Stage Automatic System for Detection of Interictal Epileptiform Discharges from Scalp Electroencephalograms.eNeuro · 2023Article
- Electrical Stimulation Induced Current Distribution in Peripheral Nerves Varies Significantly with the Extent of Nerve Damage: A Computational Study Utilizing Convolutional Neural Network and Realistic Nerve Models.International journal of neural systems · 2023Article
- Comprehensive Analysis of Feature Extraction Methods for Emotion Recognition from Multichannel EEG Recordings.Sensors (Basel, Switzerland) · 2023Article
- Improving automated diagnosis of epilepsy from EEGs beyond IEDs.Journal of neural engineering · 2022Article
- Epileptic Seizure Detection Using Geometric Features Extracted from SODP Shape of EEG Signals and AsyLnCPSO-GA.Entropy (Basel, Switzerland) · 2022Article
- Scalp EEG recordings of pediatric epilepsy patients: A dataset for automatic detection of interictal epileptiform discharges from routine EEG.Data in brief · 2021Article
- Time-Frequency Decomposition of Scalp Electroencephalograms Improves Deep Learning-Based Epilepsy Diagnosis.International journal of neural systems · 2021Article
- Automated Adult Epilepsy Diagnostic Tool Based on Interictal Scalp Electroencephalogram Characteristics: A Six-Center Study.International journal of neural systems · 2021Article
- Deep Learning for Interictal Epileptiform Spike Detection from scalp EEG frequency sub bands.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2020Article
- Decoding Three Different Preference Levels of Consumers Using Convolutional Neural Network: A Functional Near-Infrared Spectroscopy Study.Frontiers in human neuroscience · 2020Article
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10 authors.
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Abstract
Visual evaluation of electroencephalogram (EEG) for Interictal Epileptiform Discharges (IEDs) as distinctive biomarkers of epilepsy has various limitations, including time-consuming reviews, steep learning curves, interobserver variability, and the need for specialized experts. The development of an automated IED detector is necessary to provide a faster and reliable diagnosis of epilepsy. In this paper, we propose an automated IED detector based on Convolutional Neural Networks (CNNs). We have evaluated the proposed IED detector on a sizable database of 554 scalp EEG recordings (84 epileptic patients and 461 nonepileptic subjects) recorded at Massachusetts General Hospital (MGH), Boston. The proposed CNN IED detector has achieved superior performance in comparison with conventional methods with a mean cross-validation area under the precision-recall curve (AUPRC) of 0.838[Formula: see text]±[Formula: see text]0.040 and false detection rate of 0.2[Formula: see text]±[Formula: see text]0.11 per minute for a sensitivity of 80%. We demonstrated the proposed system to be noninferior to 30 neurologists on a dataset from the Medical University of South Carolina (MUSC). Further, we clinically validated the system at National University Hospital (NUH), Singapore, with an agreement accuracy of 81.41% with a clinical expert. Moreover, the proposed system can be applied to EEG recordings with any arbitrary number of channels.
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