scholarly journals Breakthrough spikes in rapid eye movement sleep from the epilepsy monitoring unit are associated with peak seizure frequency

SLEEP ◽  
2019 ◽  
Vol 43 (5) ◽  
Author(s):  
Marna B McKenzie ◽  
Michelle-Lee Jones ◽  
Aoife O’Carroll ◽  
Demitre Serletis ◽  
Leigh Anne Shafer ◽  
...  

Abstract Study Objectives Rapid eye movement sleep (REM) usually suppresses interictal epileptiform discharges (IED) and seizures. However, breakthrough IEDs in REM sometimes continue. We aimed to determine if the amount of IED and seizures in REM, or REM duration, is associated with clinical trajectories. Methods Continuous electroencephalogram (EEG) recordings from the epilepsy monitoring unit (EMU) were clipped to at least 3 h of concatenated salient findings per day including all identified REM. Concatenated EEG files were analyzed for nightly REM duration and the “REM spike burden” (RSB), defined as the proportion of REM occupied by IED or seizures. Patient charts were reviewed for clinical data, including patient-reported peak seizure frequency. Logistic and linear regressions were performed, as appropriate, to explore associations between two explanatory measures (duration of REM and RSB) and six indicators of seizure activity (clinical trajectory outcomes). Results The median duration of REM sleep was 43.3 (IQR 20.9–73.2) min per patient per night. 59/63 (93.7%) patients achieved REM during EMU admission. 39/59 (66.1%) patients had breakthrough IEDs or seizures in REM with the median RSB at 0.7% (IQR 0%–8.4%). Every 1% increase in RSB was associated with 1.69 (95% CI = 0.47–2.92) more seizures per month during the peak seizure period of one’s epilepsy (p = 0.007). Conclusions Increased epileptiform activity during REM is associated with increased peak seizure frequency, suggesting an overall poorer epilepsy trajectory. Our findings suggest that RSB in the EMU is a useful biomarker to help guide about what to expect over the course of one’s epilepsy.

Author(s):  
Duong Nhu ◽  
Mubeen Janmohamed ◽  
Lubna Shakhatreh ◽  
Ofer Gonen ◽  
Patrick Kwan ◽  
...  

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.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jan Pyrzowski ◽  
Jean- Eudes Le Douget ◽  
Amal Fouad ◽  
Mariusz Siemiński ◽  
Joanna Jędrzejczak ◽  
...  

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.


2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Minji Lee ◽  
Benjamin Baird ◽  
Olivia Gosseries ◽  
Jaakko O. Nieminen ◽  
Melanie Boly ◽  
...  

2010 ◽  
Vol 110 (5) ◽  
pp. 1283-1289 ◽  
Author(s):  
George A. Mashour ◽  
William J. Lipinski ◽  
Lisa B. Matlen ◽  
Amanda J. Walker ◽  
Ashley M. Turner ◽  
...  

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