Occurrence of epileptiform discharges and sleep during EEG recordings in children after melatonin intake versus sleep-deprivation

2015 ◽  
Vol 126 (8) ◽  
pp. 1493-1497 ◽  
Author(s):  
Greta Gustafsson ◽  
Anders Broström ◽  
Martin Ulander ◽  
Magnus Vrethem ◽  
Eva Svanborg
Keyword(s):  
Sleep Deprivation ◽  
Epileptiform Discharges ◽  
Eeg Recordings

scholarly journals EEG recording after sleep deprivation in a series of patients with juvenile myoclonic epilepsy

2005 ◽  
Vol 63 (2b) ◽  
pp. 383-388 ◽  
Author(s):  
Nise Alessandra de Carvalho Sousa ◽  
Patrícia da Silva Sousa ◽  
Eliana Garzon ◽  
Américo C. Sakamoto ◽  
Nádia I.O. Braga ◽  
...  
Keyword(s):  
Sleep Deprivation ◽  
Diagnostic Delay ◽  
Quantitative Eeg ◽  
Juvenile Myoclonic Epilepsy ◽  
Myoclonic Epilepsy ◽  
Mean Delay ◽  
Epileptiform Discharges ◽  
Number Of Patients ◽  
Syndromic Diagnosis ◽  
Eeg Recordings

Seizures in Juvenile Myoclonic Epilepsy (JME) are dependent on the sleep-wake cycle and precipitant factors, among which sleep deprivation (SD) is one of the most important. Still an under diagnosed syndrome, misinterpretation of the EEGs contributes to diagnostic delay. Despite this, a quantitative EEG investigation of SD effects has not been performed. We investigated the effect of SD on EEGs in 41 patients, aged 16-50 yr. (mean 25.4), who had not yet had syndromic diagnosis after a mean delay of 8.2 yr. Two EEG recordings separated by a 48-hour interval were taken at 7 a.m. preceded by a period of 6 hours of sleep (routine EEG) and after SD (sleep-deprived EEG). The same protocol was followed and included a rest wakefulness recording, photic stimulation, hyperventilation and a post-hyperventilation period. The EEGs were analyzed as to the effect of SD on the number, duration, morphology, localization and predominance of abnormalities in the different stages. A discharge index (DI) was calculated. Out of the 41 patients, 4 presented both normal EEG recordings. In 37 (90.2%) there were epileptiform discharges (ED). The number of patients with ED ascended from 26 (70.3%) in the routine EEG to 32 (86.5%) in the sleep-deprived exam. The presence of generalized spike-wave and multispike-wave increased from 20 (54.1%) and 13 (35.1%) in the first EEG to 29 (78.4%) and 19 (51.4%) in the second, respectively (p<0.05 and p<0.01). As to localization, the number of generalized, bilateral and synchronous ED increased from 21 (56.8%) to 30 (81.1%) (p<0.01). The DI also increased; while 8 patients (21.6%) presented greater rate in the routine EEG, 25 (67.6%) did so in the sleep-deprived EEG mainly during somnolence and sleep (p<0.01). Moreover, the paroxysms were also longer in the sleep-deprived EEG. Sleep-deprived EEG is a powerful tool in JME and can contribute significantly to the syndromic characterization of this syndrome.

scholarly journals Zero-crossing patterns reveal subtle epileptiform discharges in the scalp EEG

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jan Pyrzowski ◽  
Jean- Eudes Le Douget ◽  
Amal Fouad ◽  
Mariusz Siemiński ◽  
Joanna Jędrzejczak ◽  
...  
Keyword(s):  
Normal Subjects ◽  
Intracranial Eeg ◽  
Zero Crossing ◽  
Temporal Precision ◽  
Scalp Eeg ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Eeg Recordings ◽  
Patients With Epilepsy ◽  
Low Amplitude

AbstractClinical diagnosis of epilepsy depends heavily on the detection of interictal epileptiform discharges (IEDs) from scalp electroencephalographic (EEG) signals, which by purely visual means is far from straightforward. Here, we introduce a simple signal analysis procedure based on scalp EEG zero-crossing patterns which can extract the spatiotemporal structure of scalp voltage fluctuations. We analyzed simultaneous scalp and intracranial EEG recordings from patients with pharmacoresistant temporal lobe epilepsy. Our data show that a large proportion of intracranial IEDs manifest only as subtle, low-amplitude waveforms below scalp EEG background and could, therefore, not be detected visually. We found that scalp zero-crossing patterns allow detection of these intracranial IEDs on a single-trial level with millisecond temporal precision and including some mesial temporal discharges that do not propagate to the neocortex. Applied to an independent dataset, our method discriminated accurately between patients with epilepsy and normal subjects, confirming its practical applicability.

scholarly journals Electroencephalographic Features of Temporal Lobe Epilepsy

2010 ◽  
Vol 37 (4) ◽  
pp. 439-448 ◽  
Author(s):  
Mohammed M. Jan ◽  
Mark Sadler ◽  
Susan R. Rahey
Keyword(s):  
Temporal Lobe Epilepsy ◽  
Surgical Management ◽  
Temporal Lobe ◽  
Seizure Onset ◽  
Resective Surgery ◽  
Accurate Localization ◽  
Epileptiform Discharges ◽  
Ictal Eeg ◽  
Temporal Lobe Seizures ◽  
Eeg Recordings

Electroencephalography (EEG) is an important tool for diagnosing, lateralizing and localizing temporal lobe seizures. In this paper, we review the EEG characteristics of temporal lobe epilepsy (TLE). Several “non-standard” electrodes may be needed to further evaluate the EEG localization, Ictal EEG recording is a major component of preoperative protocols for surgical consideration. Various ictal rhythms have been described including background attenuation, start-stop-start phenomenon, irregular 2-5 Hz lateralized activity, and 5-10 Hz sinusoidal waves or repetitive epileptiform discharges. The postictal EEG can also provide valuable lateralizing information. Postictal delta can be lateralized in 60% of patients with TLE and is concordant with the side of seizure onset in most patients. When patients are being considered for resective surgery, invasive EEG recordings may be needed. Accurate localization of the seizure onset in these patients is required for successful surgical management.

scholarly journals Automated Inter-Ictal Epileptiform Discharge Detection from Routine EEG

2021 ◽  
Author(s):  
Duong Nhu ◽  
Mubeen Janmohamed ◽  
Lubna Shakhatreh ◽  
Ofer Gonen ◽  
Patrick Kwan ◽  
...  
Keyword(s):  
Royal Melbourne Hospital ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Model Variant ◽  
Eeg Recordings ◽  
Generalized Epilepsy ◽  
Electroencephalogram Eeg ◽  
Health Hospital ◽  
Epilepsy Monitoring

Epilepsy is the most common neurological disorder. The diagnosis commonly requires manual visual electroencephalogram (EEG) analysis which is time-consuming. Deep learning has shown promising performance in detecting interictal epileptiform discharges (IED) and may improve the quality of epilepsy monitoring. However, most of the datasets in the literature are small (n≤100) and collected from single clinical centre, limiting the generalization across different devices and settings. To better automate IED detection, we cross-evaluated a Resnet architecture on 2 sets of routine EEG recordings from patients with idiopathic generalized epilepsy collected at the Alfred Health Hospital and Royal Melbourne Hospital (RMH). We split these EEG recordings into 2s windows with or without IED and evaluated different model variants in terms of how well they classified these windows. The results from our experiment showed that the architecture generalized well across different datasets with an AUC score of 0.894 (95% CI, 0.881–0.907) when trained on Alfred’s dataset and tested on RMH’s dataset, and 0.857 (95% CI, 0.847–0.867) vice versa. In addition, we compared our best model variant with Persyst and observed that the model was comparable.

scholarly journals Quantitative Measure to Differentiate Wicket Spike from Interictal Epileptiform Discharges

2021 ◽  
Vol 4 (1) ◽  
pp. 14-22
Author(s):  
Suryani Gunadharma ◽  
Ahmad Rizal ◽  
Rovina Ruslami ◽  
Tri Hanggono Achmad ◽  
See Siew Ju ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Quantitative Measure ◽  
Linear Discriminant ◽  
Sharp Waves ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Temporal Location ◽  
Eeg Recordings ◽  
Excellent Accuracy

A number of benign EEG patterns are often misinterpreted as interictal epileptiform discharges (IEDs) because of their epileptiform appearances, one of them is wicket spike. Differentiating wicket spike from IEDs may help in preventing epilepsy misdiagnosis. The temporal location of IEDs and wicket spike were chosen from 143 EEG recordings. Amplitude, duration and angles were measured from the wave triangles and were used as the variables. In this study, linear discriminant analysis is used to create the formula to differentiate wicket spike from IEDs consisting spike and sharp waves. We obtained a formula with excellent accuracy. This study emphasizes the need for objective criteria to distinguish wicket spike from IEDs to avoid misreading of the EEG and misdiagnosis of epilepsy.

scholarly journals Amplitude-integrated EEG amplitudes differ between wakefulness and sleep states in children

2021 ◽  
Author(s):  
Verena Tamara Loeffelhardt ◽  
Adela Della Marina ◽  
Sandra Greve ◽  
Hanna Mueller ◽  
Ursula Felderhoff-Mueser ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Antiepileptic Drugs ◽  
Deep Sleep ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Continuous Background ◽  
Eeg Recordings ◽  
Physiological Background ◽  
Light Sleep

Introduction: Interpretation of pediatric amplitude-integrated EEG (aEEG) is hindered by the lack of knowledge on physiological background patterns in children. The aim of this study was to assess the amplitudes and bandwidths of background patterns during wakefulness and sleep in children from long-term EEGs. Methods: Forty long-term EEGs from patients < 18 years of age without or only solitary interictal epileptiform discharges were converted into aEEGs. Upper and lower amplitudes (μV) of the C3 - C4, P3 - P4, C3 - P3, C4 - P4, and Fp1 - Fp2 channels were measured during wakefulness and sleep. Bandwidths (BW, μV) were calculated, and sleep states assessed during the episodes of interest. A sensitivity analysis excluded patients who received antiepileptic drugs. Results: Median age was 9.9 years (interquartile range 6.1 - 14.7). All patients displayed continuous background patterns. Amplitudes and BW differed between wakefulness (C3 - C4 channel: upper 35 (27 - 49), lower 13 (10 - 19), BW 29 (21 - 34)) and sleep. During sleep, episodes with high amplitudes (upper 99 (71 - 125), lower 35 (25 - 44), BW 63 (44 - 81)) corresponded to sleep states N2 - N4. These episodes were interrupted by low amplitudes that were the dominating background pattern towards the morning (upper 39 (30 - 51), lower 16 (11 - 20), BW 23 (19 - 31), sleep states REM, N1, and N2). With increasing age, amplitudes and bandwidths declined. The sensitivity analysis yielded no differences in amplitude values or bandwidths. Conclusion: aEEG amplitudes and bandwidths were low during wakefulness and light sleep and high during deep sleep in stable children undergoing 24 hour EEG recordings. aEEG values were not altered by antiepileptic drugs in this study.

Automatic Detection of Interictal Epileptiform Discharges Based on the Similarity of Time-Series Sequence

2012 ◽  
Vol 239-240 ◽  
pp. 921-931
Author(s):  
Jian Zhang ◽  
Jun Zhong Zou ◽  
Lan Lan Chen ◽  
Chen Jie Zhao ◽  
Gui Song Wang
Keyword(s):  
Time Series ◽  
Slow Wave ◽  
Digital Signal ◽  
Sharp Wave ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Novel Approach ◽  
Recording Process ◽  
Eeg Recordings ◽  
Maximum Similarity

In this paper, an effective digital signal processing method based on the merger of the increasing and decreasing time-series sequences (MIDS) is introduced. On the basis of the merging of EEG signals, a new IED (Interictal Epileptiform Discharges) detection method is proposed. The first step of this new method is to establish a database by selecting peaked wave fragments. Then, the similarity between pending test fragment and peaked wave samples in the database is calculated. When the maximum similarity is greater than a certain threshold, the fragment is judged to be a peaked wave. Finally, the wave type i.e. spike wave, sharp wave, spike-and-slow wave or sharp-and-slow wave can be determined by whether there is a subsequent slow wave or not. Continuous sharp wave can be viewed as spike rhythm. In this research, 92 IED fragments from 4 suspected epilepsy patients are collected to establish the sample database. The proposed method was tested on EEG recordings from other 31 suspected patients. The results show that 98.11% of the IED fragments marked by doctors were detected. The experimental results show that this method performs well at IED detection in the clinical EEG data. The similarity is measured based on the comparison between fragments of different time length and can be viewed as a novel approach for the detection of typical EEG waveform. This research draws two conclusions: (1) the waveform of individual peaked wave is stable during 24-hour EEG recording process; (2) the database containing a small number peaked wave samples can be used to detect IED fragments.

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