DeepIED: An epileptic discharge detector for EEG-fMRI based on deep learning

Presurgical evaluation that can precisely delineate the epileptogenic zone (EZ) is one important step for successful surgical resection treatment of refractory epilepsy patients. The noninvasive EEG-fMRI recording technique combined with general linear model (GLM) analysis is considered an important tool for estimating the EZ. However, the manual marking of interictal epileptic discharges (IEDs) needed in this analysis is challenging and time-consuming because the quality of the EEG recorded inside the scanner is greatly deteriorated compared to the usual EEG obtained outside the scanner. This is one of main impediments to the widespread use of EEG-fMRI in epilepsy. We propose a deep learning based semi-automatic IED detector that can find the candidate IEDs in the EEG recorded inside the scanner which resemble sample IEDs marked in the EEG recorded outside the scanner. The manual marking burden is greatly reduced as the expert need only edit candidate IEDs. The model is trained on data from 30 patients. Validation of IEDs detection accuracy on another 37 consecutive patients shows our method can improve the median sensitivity from 50.0% for the previously proposed template-based method to 84.2%, with false positive rate as 5 events/min. Reproducibility validation on 15 patients is applied to evaluate if our method can produce similar hemodynamic response maps compared with the manual marking ground truth results. We explore the concordance between the maximum hemodynamic response and the intracerebral EEG defined EZ and find that both methods produce similar percentage of concordance (76.9%, 10 out of 13 patients, electrode was absent in the maximum hemodynamic response in two patients). This tool will make EEG-fMRI analysis more practical for clinical usage. Keywords: EEG-fMRI, Deep learning, IED detection, GLM, Epileps

Sleep influences the intracerebral EEG pattern of focal cortical dysplasia

We analyze the distribution of intracerebral EEG patterns of FCD in relation to sleep. FCD interictal EEG patterns are present between 45% and 97% of the time analyzed. Despite almost continuous spiking, sleep is an important modulator of FCD EEG patterns. This suggests that dysplastic tissue is under thalamocortical control. Objective: Focal cortical dysplasia (FCD) is able to generate an intrinsic pathological EEG activity characterized by a continuous or near-continuous spiking. Different patterns of discharge were described. We examined quantitatively the distribution of the intracerebral FCD patterns in relation to sleep in order to investigate whether this activity is independent of thalamocortical influences. Methods: We analyzed the first sleep cycle of 5 patients with a diagnosis of FCD type II who underwent combined scalp-intracranial electroencephalography (EEG), and showed an intracranial EEG pattern typical for FCD. Three patterns of FCD intracranial EEG activity were identified in all 5 patients, and visually marked for a maximum of 30. min of each stage (wake, N1, N2, N3, REM): spike or polyspike exceeding 2. Hz (pattern 1), spike or polyspike interrupted by flat periods below 2. Hz (pattern 2) and discharges of >15. Hz low-voltage rhythmic activity with regular morphology (pattern 3). After marking, the percentages of the three patterns across the different stages were calculated. Results: The three patterns of FCD were present between 45% and 97% of the total time analyzed. Pattern 1 was the predominant pattern in wakefulness (73-100%), N1 (76-97%) and N2 (58-88.5%) in all patients, and in REM in 4 of 5 patients (91-100%). During N2 and N3, there was an increase in pattern 2 in all patients, becoming the predominant pattern in 3 of the 5 patients during N3 (63-89%). Pattern 3 was rare and only sporadically observed during N2 and N3. Wakefulness and REM sleep showed a similar pattern (pattern 1) with a slight amplitude reduction in REM sleep. Significance: Despite the presence of an almost continuous discharge, sleep is an important modulator of the pathological EEG patterns found in FCD type II. This might suggest that dysplastic tissue is influenced by the thalamo-cortical control mechanisms involved in the generation of sleep.Fil: Menezes Cordeiro, Inês. McGill University; Canadá. Faro Hospital; PortugalFil: Von Ellenrieder, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. McGill University; CanadáFil: Zazubovits, Natalja. McGill University; CanadáFil: Dubeau, François. McGill University; CanadáFil: Gotman, Jean. McGill University; CanadáFil: Frauscher, Birgit. McGill University; Canadá. Universidad de Innsbruck; Austri