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The detection of interictal epileptiform discharge (IED) is crucial for the diagnosis of epilepsy, but automated methods often lack interpretability. This study proposes IED-RAG, an explainable multimodal framework for joint IED detection…

Signal Processing · Electrical Eng. & Systems 2026-02-17 Yu Zhu , Jiayang Guo , Jun Jiang , Peipei Gu , Xin Shu , Duo Chen

Approximately over 50 million people worldwide suffer from epilepsy. Traditional diagnosis of epilepsy relies on tedious visual screening by highly trained clinicians from lengthy EEG recording that contains the presence of seizure (ictal)…

Artificial Intelligence · Computer Science 2009-04-27 Forrest Sheng Bao , Jue-Ming Gao , Jing Hu , Donald Y. -C. Lie , Yuanlin Zhang , K. J. Oommen

The diagnosis of epilepsy generally includes a visual inspection of EEG recorded data by the Neurologist, with the purpose of checking the occurrence of transient waveforms called interictal epileptiform discharges. These waveforms have…

Computer Vision and Pattern Recognition · Computer Science 2016-06-08 Andre Rosado , Agostinho C Rosa

Epilepsy is one of the most common neurological disorders that greatly impair patient' daily lives. Traditional epileptic diagnosis relies on tedious visual screening by neurologists from lengthy EEG recording that requires the presence of…

Artificial Intelligence · Computer Science 2016-11-18 Forrest Sheng Bao , Donald Yu-Chun Lie , Yuanlin Zhang

Epilepsy is a neurological disorder and for its detection, encephalography (EEG) is a commonly used clinical approach. Manual inspection of EEG brain signals is a time-consuming and laborious process, which puts heavy burden on neurologists…

Computer Vision and Pattern Recognition · Computer Science 2018-01-17 Ihsan Ullah , Muhammad Hussain , Emad-ul-Haq Qazi , Hatim Aboalsamh

The presence of interictal epileptiform discharges (IEDs) in electroencephalogram (EEG) recordings is a critical biomarker of epilepsy. Even trained neurologists find detecting IEDs difficult, leading many practitioners to turn to machine…

Epilepsy is the second most common brain disorder after migraine. Automatic detection of epileptic seizures can considerably improve the patients' quality of life. Current Electroencephalogram (EEG)-based seizure detection systems encounter…

Signal Processing · Electrical Eng. & Systems 2018-03-28 Ramy Hussein , Hamid Palangi , Rabab Ward , Z. Jane Wang

Magnetoencephalography (MEG) allows the non-invasive detection of interictal epileptiform discharges (IEDs). Clinical MEG analysis in epileptic patients traditionally relies on the visual identification of IEDs, which is time consuming and…

Epilepsy is a prevalent neurological disorder marked by sudden, brief episodes of excessive neuronal activity caused by abnormal electrical discharges, which may lead to some mental disorders. Most existing deep learning methods for…

Machine Learning · Computer Science 2025-10-16 Zexin Wang , Lin Shi , Haoyu Wu , Junru Luo , Xiangzeng Kong , Jun Qi

Epileptic seizures are neurological disorders characterized by abnormal and excessive electrical activity in the brain, resulting in recurrent seizure events. Electroencephalogram (EEG) signals are widely used for seizure diagnosis due to…

Machine Learning · Computer Science 2026-04-02 Ferdaus Anam Jibon , Fazlul Hasan Siddiqui , F. Deeba , Gahangir Hossain

Epilepsy is one of the most common neurological disorders that can be diagnosed through electroencephalogram (EEG), in which the following epileptic events can be observed: pre-ictal, ictal, post-ictal, and interictal. In this paper, we…

Machine Learning · Computer Science 2021-02-12 Jefferson Tales Oliva , João Luís Garcia Rosa

In electroencephalogram (EEG) recordings, the presence of interictal epileptiform discharges (IEDs) serves as a critical biomarker for seizures or seizure-like events.Detecting IEDs can be difficult; even highly trained experts disagree on…

Epilepsy is the fourth most common neurological disorder, affecting about 1% of the population at all ages. As many as 60% of people with epilepsy experience focal seizures which originate in a certain brain area and are limited to part of…

Machine Learning · Computer Science 2019-03-20 Diyuan Lu , Jochen Triesch

Timely diagnosis is important for saving the life of epileptic patients. In past few years, a lot of treatments are available for epilepsy. These treatments require use of anti-seizure drugs but are not effective in controlling frequency of…

Machine Learning · Computer Science 2021-11-08 Shivam Gupta , Jyoti Meena , O. P Gupta

Epilepsy is one of the most serious neurological diseases, affecting 1-2% of the world's population. The diagnosis of epilepsy depends heavily on the recognition of epileptic waves, i.e., disordered electrical brainwave activity in the…

Signal Processing · Electrical Eng. & Systems 2023-06-26 Junru Chen , Yang Yang , Tao Yu , Yingying Fan , Xiaolong Mo , Carl Yang

Diagnosing epilepsy is challenging when routine EEGs lack interictal epileptiform discharges (IEDs). Intermittent photic stimulation (IPS) and hyperventilation (HV) can increase diagnostic yield, but their interpretation is subjective. We…

Signal Processing · Electrical Eng. & Systems 2026-05-25 Giacomo Zanardini , Ryan Moesman , Paul van der Kleij , Robert van den Berg , Justin Dauwels

Electrophysiological observation plays a major role in epilepsy evaluation. However, human interpretation of brain signals is subjective and prone to misdiagnosis. Automating this process, especially seizure detection relying on scalp-based…

Machine Learning · Computer Science 2018-07-06 David Ahmedt-Aristizabal , Clinton Fookes , Kien Nguyen , Sridha Sridharan

Epilepsy, affecting approximately 50 million people globally, is characterized by abnormal brain activity and remains challenging to treat. The diagnosis of epilepsy relies heavily on electroencephalogram (EEG) data, where specialists…

Objective: Epilepsy is a chronic neurological disorder characterized by the occurrence of spontaneous seizures, which affects about one percent of the world's population. Most of the current seizure detection approaches strongly rely on…

Signal Processing · Electrical Eng. & Systems 2020-02-04 Xiang Zhang , Lina Yao , Manqing Dong , Zhe Liu , Yu Zhang , Yong Li

Identifying seizure activities in non-stationary electroencephalography (EEG) is a challenging task, since it is time-consuming, burdensome, and dependent on expensive human resources and subject to error and bias. A computerized seizure…

Signal Processing · Electrical Eng. & Systems 2020-04-29 S. Sheykhivand , T. Yousefi Rezaii , Z. Mousavi , A. Delpak , A. Farzamnia
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