What is the role for EEG after sleep deprivation in the diagnosis of epilepsy? Issues, controversies, and future directions

Giorgi, Filippo Sean; Guida, Melania; Caciagli, Lorenzo; Maestri, Michelangelo; Carnicelli, Luca; Bonanni, Enrica; Bonuccelli, Ubaldo

doi:10.1016/j.neubiorev.2014.10.005

Cited by 21 publications

(25 citation statements)

References 121 publications

(187 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Another issue raised withn reference to the characteristic neural discharges is sleep deprivation. In an earlier work by Giorgi et al [35] it was found that sleep deprivation influences the higher incidence of IEDs. In the study by Giorgi et al [32], which qualified for the review, the researchers wanted to check whether morning sleep is associated with an increased incidence of IEDs, compared to physiological sleep.…”

Section: Brief Description Of the State Of Knowledgementioning

confidence: 93%

Bilateral dependence of sleep disorders and temporal lobe epilepsy – literature review

Drabik¹,

Sarnacki²,

Miziak³

2021

Med Og Nauk Zdr.

View full text Add to dashboard Cite

Introduction. The analysis of sleep and its phases in the context of various pathophysiologies has attracted increasing attention from the scientific community; therfore, this review of the literature focuses on temporal lobe epilepsy (TLE). Objective. The aim of the review is to systematize and update the knowledge and research currently available on the interplay of sleep deprivation and seizures in TLE, with a focus on MTLE. Materials and method. The following keyword combinations were used to search the Pubmed, Science Direct and Google Scholar databases: sleep disturbance in mesial temporal lobe epilepsy, sleep in temporal lobe epilepsy, nocturnal temporal lobe epilepsy, temporal epilepsy sleep disturbance. After applying exclusion criteria, 23 papers were selected for the review. Brief description of the state of knowledge. The publications qualified for the review were divided into 7 categories in terms of the subject of the publication. These categories relate to characteristic brain waves, memory and its consolidation processes, heart and sleep apnea, anti-epileptic drugs, motor disorders and lateralization of the discharges. Articles that did not fit into any of the previously mentioned categories were placed in the 'Other' group. Conclusions. It is agreed that sleep is an important factor that should be taken into account when selecting the appropriate treatment and researching new AEDs. Moreover, sleep is significant to the patient›s quality of life, has a profound impact on its components, for example, physical and social functions, emotions and leisure, but may also influence the effectiveness of therapy.

show abstract

Section: Brief Description Of the State Of Knowledgementioning

confidence: 93%

Bilateral dependence of sleep disorders and temporal lobe epilepsy – literature review

Drabik¹,

Sarnacki²,

Miziak³

2021

Med Og Nauk Zdr.

View full text Add to dashboard Cite

show abstract

“…It has been suggested that sleep deprivation results in sleep that is more activating than natural sleep alone. It is unclear whether sleep deprivation itself could be related to an activated cortical state even in the absence of sleep (for a review regarding the controversies surrounding sleep deprivation and sleep, see [24]). The use of medication to induce sleep remains controversial because of the potential effects that sedating medications can have on the EEG recording that could lead to confounders.…”

Section: Inclusion Of Sleep Deprivation and Sleep In Eeg Studiesmentioning

confidence: 99%

Electroencephalography after a single unprovoked seizure

Debicki

2017

Seizure

View full text Add to dashboard Cite

Electroencephalography (EEG) is an essential diagnostic tool in the evaluation of seizure disorders. In particular, EEG is used as an additional investigation for a single unprovoked seizure. Epileptiform abnormalities are related to seizure disorders and have been shown to predict recurrent unprovoked seizures (i.e., a clinical definition of epilepsy). Thus, the identification of epileptiform abnormalities after a single unprovoked seizure can inform treatment options. The current review addresses the relationship between EEG abnormalities and seizure recurrence. This review also addresses factors that are found to improve the yield of recording epileptiform abnormalities including timing of EEG relative to the new-onset seizure, use of repeat studies, use of sleep deprivation and prolonged recordings.

show abstract

“…EEG signals are complex; therefore, fractal dimension has been used to describe the complexity of the EEG signals (Ibáñez-Molina and Iglesias-Parro, 2014). Some researchers used EEG to detect some diseases like epileptic seizure (Giorgi et al, 2014;Guo et al, 2010). EEG signals are analyzed based on age in order to show the difference in brain signals for each group of people (Knyazev et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Profound correlation of human and NAO-robot interaction through facial expression controlled by EEG sensor

Basori¹,

Ahmed²,

Prabuwono³

et al. 2018

Int. j. adv. appl. sci.

View full text Add to dashboard Cite

Emotion recognition from brain computer interface (EEG) has been studied extensively for the past few years. Time-frequency analysis is widely used in the past research; however, a variation of case study determines the brain signal analysis. In this paper, human emotion from brain waves is recognized in simple ways by calculating a frequency of signal variation. Entirely 35 healthy subjects from students with age 18-25 years old. The students are divided into 3 groups; the first group consists of 15 students; the second group consists of 10 students and the third group consists of 10 students. Each student takes 4 seconds to test his or her internal emotions. The signal speed is recorded during those 4 seconds. Based on stimulus time, various knocks for Z1 and Z2 is observed during a particular time. The experiment can be reproduced for in upcoming future by following the procedure. There are two main elements to measure signal speed which are ΔT and gap. ΔT subject to time differentiation of the changes in time-frequency of Alpha signals. For an evaluation of this work, there is an available benchmark database of EEG labeled with emotions; it mentions that emotional strength can be used as a factor to differentiate between human emotions. The results of this paper can be compared with previous researches which use the same device to differentiate between happy and sad emotions in terms of emotional strength. There is a strong correlation between emotional strength and frequency, we proved that sad feeling is speedier and beyond steady compared to happy since the number of ΔV to Z1 which represents sad emotion of Alpha signals is greater than ΔV to Z2 that represents a happy feeling in the same time period of the interaction process.

show abstract

What is the role for EEG after sleep deprivation in the diagnosis of epilepsy? Issues, controversies, and future directions

Cited by 21 publications

References 121 publications

Bilateral dependence of sleep disorders and temporal lobe epilepsy – literature review

Bilateral dependence of sleep disorders and temporal lobe epilepsy – literature review

Electroencephalography after a single unprovoked seizure

Profound correlation of human and NAO-robot interaction through facial expression controlled by EEG sensor

Contact Info

Product

Resources

About