ArticleInternational journal of neural systems2021
Automated Adult Epilepsy Diagnostic Tool Based on Interictal Scalp Electroencephalogram Characteristics: A Six-Center Study.
Article in International journal of neural systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Development and validation of a multimodal automatic interictal epileptiform discharge detection model: a prospective multi-center study.BMC medicine · 2025Article
- Article
- An EEG dataset for interictal epileptiform discharge with spatial distribution information.Scientific data · 2025Article
- Improving diagnostic accuracy of routine EEG for epilepsy using deep learning.Brain communications · 2025Article
- Neuronal avalanches in temporal lobe epilepsy as a noninvasive diagnostic tool investigating large scale brain dynamics.Scientific reports · 2024Article
- Automatic detection of epilepsy from EEGs using a temporal convolutional network with a self-attention layer.Biomedical engineering online · 2024Article
- Automatic interictal epileptiform discharge (IED) detection based on convolutional neural network (CNN).Frontiers in molecular biosciences · 2023Article
- EEG Microstate Features as an Automatic Recognition Model of High-Density Epileptic EEG Using Support Vector Machine.Brain sciences · 2022Article
- Improving automated diagnosis of epilepsy from EEGs beyond IEDs.Journal of neural engineering · 2022Article
- Diagnosis of Epilepsy with Functional Connectivity in EEG after a Suspected First Seizure.Bioengineering (Basel, Switzerland) · 2022Article
- Epileptic Seizure Detection Using Geometric Features Extracted from SODP Shape of EEG Signals and AsyLnCPSO-GA.Entropy (Basel, Switzerland) · 2022Article
- Moving the field forward: detection of epileptiform abnormalities on scalp electroencephalography using deep learning-clinical application perspectives.Brain communications · 2022Review
- Time-Frequency Decomposition of Scalp Electroencephalograms Improves Deep Learning-Based Epilepsy Diagnosis.International journal of neural systems · 2021Article
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Authors and funding
15 authors.
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Abstract
The diagnosis of epilepsy often relies on a reading of routine scalp electroencephalograms (EEGs). Since seizures are highly unlikely to be detected in a routine scalp EEG, the primary diagnosis depends heavily on the visual evaluation of Interictal Epileptiform Discharges (IEDs). This process is tedious, expert-centered, and delays the treatment plan. Consequently, the development of an automated, fast, and reliable epileptic EEG diagnostic system is essential. In this study, we propose a system to classify EEG as epileptic or normal based on multiple modalities extracted from the interictal EEG. The ensemble system consists of three components: a Convolutional Neural Network (CNN)-based IED detector, a Template Matching (TM)-based IED detector, and a spectral feature-based classifier. We evaluate the system on datasets from six centers from the USA, Singapore, and India. The system yields a mean Leave-One-Institution-Out (LOIO) cross-validation (CV) area under curve (AUC) of 0.826 (balanced accuracy (BAC) of 76.1%) and Leave-One-Subject-Out (LOSO) CV AUC of 0.812 (BAC of 74.8%). The LOIO results are found to be similar to the interrater agreement (IRA) reported in the literature for epileptic EEG classification. Moreover, as the proposed system can process routine EEGs in a few seconds, it may aid the clinicians in diagnosing epilepsy efficiently.
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