Resting-state EEG for the diagnosis of idiopathic epilepsy and psychogenic nonepileptic seizures: A systematic review

Faiman, Irene; Smith, Stuart; Hodsoll, John; Young, Allan H.; Shotbolt, Paul

doi:10.1016/j.yebeh.2021.108047

Cited by 29 publications

(29 citation statements)

References 79 publications

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“…Epilepsy is a multiple complications of aSAH, and the main factors for inducing epilepsy are as follows: (1) oxygen-free radicals and glutamate strongly stimulate the brain, resulting in synchronous abnormal discharge [ 11 ]; (2) vasospasm and obvious ischemia; and (3) insula damage, subarachnoid hemorrhage, and brain tissue edema [ 12 ]. In the late stage of subarachnoid hemorrhage, abnormal astrocyte hyperplasia and nerve cell degeneration will occur, which will inhibit network balance and destroy the excitability of normal neurons.…”

Section: Discussionmentioning

confidence: 99%

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Prediction Value of Epilepsy Secondary to Inferior Cavity Hemorrhage Based on Scalp EEG Wave Pattern in Deep Learning

Jiang

2022

Journal of Healthcare Engineering

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Objective. To search the predictive value of epilepsy secondary to acute subarachnoid hemorrhage (aSAH) based on EEG wave pattern in deep learning. Methods. A total of 156 cases of secondary epilepsy with lower cavity hemorrhage in our hospital were selected and divided into the late epilepsy group and the early epilepsy group according to seizure time, and the nonseizure group and the seizure group according to seizure condition. General data of patients were collected, the EEG types of each group were analyzed, and the disease recurrence rate, treatment effect, and symptom onset time were compared. Results. Rapid and slow and rapid blood flow velocity were the main abnormal manifestations of epilepsy secondary to inferior cavity hemorrhage, accounting for 33.3% and 18.6%, respectively. Compared with the seizure group, the proportion of type ii and type iii in the nonseizure group was higher, and the proportion of type ii and type iii in the early epilepsy group was higher than in the late epilepsy group ( P < 0.05). The diagnostic accuracy, missed diagnosis rate, misdiagnosis rate, specificity, and sensitivity of the EEG wave pattern were 94.9%, 3.2%, 1.9%, 91.7%, and 96.2%, respectively. Compared with the early epilepsy group, the recurrence rate of type iii and type iv in the late epilepsy group was higher ( P < 0.05). The effective rates of the attack group and the nonattack group were 72.7% and 97.0%, respectively. Compared with the attack group, the effective rate of the nonattack group was higher ( P < 0.05). The effective rates of the early epilepsy group and the late epilepsy group were 91.7% and 85.0%, respectively. Compared with the late epilepsy group, the effective rate of the early epilepsy group was higher ( P < 0.05). Compared with the early epilepsy group, the late epilepsy group had longer tonic-clonic seizures, atonic seizures, and absent seizures, and the difference between the groups was statistically significant ( P < 0.05). Conclusion. In aSAH secondary epilepsy disease prediction, based on indepth study of the scalp EEG wave type prediction, they play an important role, including aSAH high-risk secondary epilepsy wave types for V, III, and IV types, as well as early and late epilepsy associated with disease stage. Through the diagnosis method to predict the severity of disease, this builds a good foundation for clinical treatment. It is beneficial to improve the effective rate of treatment.

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Section: Discussionmentioning

confidence: 99%

“…discharge [11]; (2) vasospasm and obvious ischemia; and (3) insula damage, subarachnoid hemorrhage, and brain tissue edema [12]. In the late stage of subarachnoid hemorrhage, abnormal astrocyte hyperplasia and nerve cell degeneration will occur, which will inhibit network balance and destroy the excitability of normal neurons.…”

Section: Total=736mentioning

confidence: 99%

Prediction Value of Epilepsy Secondary to Inferior Cavity Hemorrhage Based on Scalp EEG Wave Pattern in Deep Learning

Jiang

2022

Journal of Healthcare Engineering

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“…Automated extraction of new epilepsy markers on routine EEG could lead to reduced rate of misdiagnosis, increased availability in areas without access to an expert neurophysiologist, and more efficient clinical trials. Research on automatic analysis of EEG data is thriving, in part assisted by the recent increase in computational capacities [43][44][45][46][47][48][49][50] . However, automatic analysis of EEG is not mentioned in any of the high-quality clinical practice guidelines systematically reviewed by the ILAE 17 .…”

Section: Discussionmentioning

confidence: 99%

Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: protocol for a systematic review

Lemoine

Briard

Rioux

et al. 2022

Preprint

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Background: The diagnosis of epilepsy frequently relies on the visual interpretation of the electroencephalogram (EEG) by a neurologist. The hallmark of epilepsy on EEG is the interictal epileptiform discharge (IED). This marker lacks sensitivity: it is only captured in a small percentage of 30-minute routine EEGs in patients with epilepsy. In the past three decades, there has been growing interest in the use of computational methods to analyze the EEG without relying on the detection of IEDs, but none have made it to the clinical practice. We aim to review the diagnostic accuracy of quantitative methods applied to ambulatory EEG analysis to guide the diagnosis and management of epilepsy. Methods: The protocol complies with the recommendations for systematic reviews of diagnostic test accuracy by Cochrane. We will search MEDLINE, EMBASE, EBM reviews, IEEE Explore along with grey literature for articles, conference papers and conference abstracts published after 1961. We will include observational studies that present a computational method to analyze the EEG for the diagnosis of epilepsy in adults or children without relying on the identification of IEDs or seizures. The reference standard is the diagnosis of epilepsy by a physician. We will report the estimated pooled sensitivity and specificity, and receiver operating characteristic area-under-the-curve (ROC AUC) for each marker. If possible, we will perform a meta-analysis of the sensitivity and specificity and ROC AUC for each individual marker. We will assess the risk of bias using an adapted QUADAS-2 tool. We will also describe the algorithms used for signal processing, feature extraction and predictive modeling, and comment on the reproducibility of the different studies. Discussion: Despite the promise to unveil epileptiform patterns that cannot be seen by the naked eye, computational analysis of ambulatory EEG has not yet been successfully translated to the clinical setting. We hope to produce recommendations for future studies on computer-assisted EEG interpretation for the diagnosis and management of epilepsy. Systematic review registration: PROSPERO #292261

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“…In addition, EEG signals are characterized by a high temporal resolution, which is crucial for the analysis of fast brain functional dynamics at different frequency ranges (1–4 Hz, delta; 4–8 Hz theta; 8–12 Hz, alpha; 12–30 Hz, beta; and >30 Hz, gamma). On the other hand, various studies in the literature demonstrated the potential use of EEG signals to identify different abnormal brain conditions, including depression [ 10 , 11 , 12 ], seizures [ 13 , 14 , 15 ] and NDD, such as Parkinson’s disease [ 16 , 17 , 18 ] and AD [ 9 , 18 , 19 , 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Weighted Brain Network Analysis on Different Stages of Clinical Cognitive Decline

et al. 2022

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This study addresses brain network analysis over different clinical severity stages of cognitive dysfunction using electroencephalography (EEG). We exploit EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients and Alzheimer’s disease (AD) patients. We propose a new framework to study the topological networks with a spatiotemporal entropy measure for estimating the connectivity. Our results show that functional connectivity and graph analysis are frequency-band dependent, and alterations start at the MCI stage. In delta, the SCI group exhibited a decrease of clustering coefficient and an increase of path length compared to MCI and AD. In alpha, the opposite behavior appeared, suggesting a rapid and high efficiency in information transmission across the SCI network. Modularity analysis showed that electrodes of the same brain region were distributed over several modules, and some obtained modules in SCI were extended from anterior to posterior regions. These results demonstrate that the SCI network was more resilient to neuronal damage compared to that of MCI and even more compared to that of AD. Finally, we confirm that MCI is a transitional stage between SCI and AD, with a predominance of high-strength intrinsic connectivity, which may reflect the compensatory response to the neuronal damage occurring early in the disease process.

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Resting-state EEG for the diagnosis of idiopathic epilepsy and psychogenic nonepileptic seizures: A systematic review

Cited by 29 publications

References 79 publications

Prediction Value of Epilepsy Secondary to Inferior Cavity Hemorrhage Based on Scalp EEG Wave Pattern in Deep Learning

Prediction Value of Epilepsy Secondary to Inferior Cavity Hemorrhage Based on Scalp EEG Wave Pattern in Deep Learning

Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: protocol for a systematic review

Weighted Brain Network Analysis on Different Stages of Clinical Cognitive Decline

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