Consequences of Epilepsy through the Ages: When is the Die Cast?

Holmes, Gregory L.

doi:10.5698/1535-7511-12.4s.4

Cited by 10 publications

(7 citation statements)

References 47 publications

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“…In general, the mean LOSO CV results across different EEG features improves by including the age and vigilance state of the patient. The improvement in classification results indicates that patient information such as age and vigilance state affect the pathogenesis of epilepsy (epileptogenic potential) as shown by earlier studies [25,32,33].…”

Section: Classification Of Epileptic Eegs With Ieds Vs Non-epileptic ...mentioning

confidence: 52%

“…Though epilepsy affects people of all ages, the person's age has a significant impact on epilepsy [25]. Children and older individuals are at greater risk of developing epilepsy than young and middle-aged people [32]. Klimes et al have reported that the state of vigilance has an effect in localizing the epileptogenic zone in interictal intracranial EEG [33].…”

Section: Introductionmentioning

confidence: 99%

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Improving automated diagnosis of epilepsy from EEGs beyond IEDs

Thangavel

Thomas²,

Sinha³

et al. 2022

J. Neural Eng.

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Objective: Clinical diagnosis of epilepsy relies partially on identifying Interictal Epileptiform Discharges (IEDs) in scalp electroencephalograms (EEGs). This process is expert-biased, tedious, and can delay the diagnosis procedure. Beyond automatically detecting IEDs, there are far fewer studies on automated methods to differentiate epileptic EEGs (potentially without IEDs) from normal EEGs. In addition, the diagnosis of epilepsy based on a single EEG tends to be low. Consequently, there is a strong need for automated systems for EEG interpretation. Traditionally, epilepsy diagnosis relies heavily on IEDs. However, since not all epileptic EEGs exhibit IEDs, it is essential to explore IEDindependent EEG measures for epilepsy diagnosis. The main objective is to develop an automated system for detecting epileptic EEGs, both with or without IEDs. In order to detect epileptic EEGs without IEDs, it is crucial to include EEG features in the algorithm that are not directly related to IEDs. Approach: In this study, we explore the background characteristics of interictal EEG for automated and more reliable diagnosis of epilepsy. Specifically, we investigate features based on univariate temporal measures (UTM), spectral, wavelet, Stockwell, connectivity, and graph metrics of EEGs, besides patient-related information (age and vigilance state). The evaluation is performed on a sizeable cohort of routine scalp EEGs (685 epileptic EEGs and 1229 normal EEGs) from five centers across Singapore, USA, and India. Main results: In comparison with the current literature, we obtained an improved Leave-One-Subject-Out (LOSO) cross-validation (CV) area under the curve (AUC) of 0.871 (Balanced Accuracy (BAC) of 80.9%) with a combination of 3 features (IED rate, and Daubechies and Morlet wavelets) for the classification of EEGs with IEDs vs. normal EEGs. The IED-independent feature UTM achieved a LOSO CV AUC of 0.809 (BAC of 74.4%). The inclusion of IED-independent features also helps to improve the EEG-level classification of epileptic EEGs with and without IEDs vs. normal EEGs, achieving an AUC of 0.822 (BAC of 77.6%) compared to 0.688 (BAC of 59.6%) for classification only based on the IED rate. Specifically, the addition of IED-independent features improved the BAC by 21% in detecting epileptic EEGs that do not contain IEDs. Significance: These results pave the way towards automated detection of epilepsy. We are one of the first to analyse epileptic EEGs without IEDs, thereby opening up an underexplored option in epilepsy diagnosis.

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Section: Classification Of Epileptic Eegs With Ieds Vs Non-epileptic ...mentioning

confidence: 52%

Section: Introductionmentioning

confidence: 99%

Improving automated diagnosis of epilepsy from EEGs beyond IEDs

Thangavel

Thomas²,

Sinha³

et al. 2022

J. Neural Eng.

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“…Examining such patterns of responses in relation to epilepsy-related factors (e.g., seizure frequency, medication adherence, comorbidity) and social factors (e.g., employment status, social isolation, access to mental healthcare) might identify high-risk groups who would benefit from tailored interventions. That such a high percentage of adults with inactive epilepsy still report SPD suggests that epilepsy continues to have a long-term impact even after the seizures no longer occur [22]. The difference in SPD prevalence between whites with inactive and no epilepsy requires more study.…”

Section: Discussionmentioning

confidence: 99%

Serious psychological distress among adults with active epilepsy in all racial/ethnic groups and among adults with inactive epilepsy in non-Hispanic whites is significantly higher than among adults without epilepsy—U.S. National Health Interview Survey, 2010, 2013, 2015, and 2017

Kobau

Sapkota

Zack

2019

Epilepsy & Behavior

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Serious psychological distress (SPD) includes mental health problems severe enough to cause moderate-to-serious impairment in daily activities and to require treatment. Serious psychological distress is based on answers to six survey questions from the Kessler-6 scale used internationally in public health surveillance systems to assess recent feelings of sadness, restlessness, hopelessness, nervousness, worthlessness, and the sense that everything is an effort. We combined nationally representative samples in the

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“…It affects individuals across all age groups, with a higher incidence observed in children compared to youth and middle-aged individuals, and an even more pronounced prevalence in the elderly. Consequently, the incidence curve exhibits a U-shape, which significantly increases and transitions to a J-shape after the age of 60 [2,4,5].…”

Section: Introductionmentioning

confidence: 99%

A Real-World Safety Profile in Neurological, Skin, and Sexual disorders of Antiepileptic Drugs Using Pharmacovigilance Database of the Korea Adverse Event Reporting System (KAERS)

Kim,

Lee

2024

Preprint

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This study aims to examine the safety profile of antiepileptic drugs (AEDs) using real-world data, with a focus on neurological, skin, and sexual/reproductive disorders. Data were collected from AED-caused reports in the Korea Adverse Event Reporting System Database (KAERS-DB) from 2012 to 2022. Totally, 46,963 adverse drug reaction (ADR)-drug pairs were analyzed. At the system organ class level, the most frequently reported classes for sodium channel blockers (SCBs) were skin (37.9%), neurological (16.7%), and psychiatric disorders (9.7%). However, for non-SCBs, these were neurological (31.2%), gastrointestinal (22.0%), and psychiatric disorders (18.2%). The most common ADRs induced by SCBs were rash (17.8%), pruritus (8.2%), and dizziness (6.7%). In contrast, non-SCBs induced dizziness (23.7%), somnolence (13.0%), and nausea (6.3%). Among the most commonly reported ADRs, rash, pruritus, and urticaria occurred on average two days later with SCBs compared to non-SCBs. Sexual/reproductive disorders were reported at a frequency of 0.23%. SCBs were reported as the cause more frequently than non-SCBs (59.8% vs. 40.2%, Fisher’s exact test, p&lt;0.0001). Based on real-world data, we identified the safety profiles of AEDs. AED-induced ADRs exhibited different patterns depending on the mechanism. Therefore, it is important to establish different pharmacovigilance strategies to ensure proper monitoring.

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Consequences of Epilepsy through the Ages: When is the Die Cast?

Cited by 10 publications

References 47 publications

Improving automated diagnosis of epilepsy from EEGs beyond IEDs

Improving automated diagnosis of epilepsy from EEGs beyond IEDs

Serious psychological distress among adults with active epilepsy in all racial/ethnic groups and among adults with inactive epilepsy in non-Hispanic whites is significantly higher than among adults without epilepsy—U.S. National Health Interview Survey, 2010, 2013, 2015, and 2017

A Real-World Safety Profile in Neurological, Skin, and Sexual disorders of Antiepileptic Drugs Using Pharmacovigilance Database of the Korea Adverse Event Reporting System (KAERS)

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