Scalp EEG recordings of pediatric epilepsy patients: A dataset for automatic detection of interictal epileptiform discharges from routine EEG

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.

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Predictive Value of Interictal Epileptiform Discharges During Non-REM Sleep on Scalp EEG Recordings for the Lateralization of Epileptogenesis

Epilepsia ◽

10.1111/j.1528-1157.1998.tb01431.x ◽

1998 ◽

Vol 39 (6) ◽

pp. 628-632 ◽

Cited By ~ 57

Author(s):

Naoto Adachi ◽

Gonzalo Alarcon ◽

Colin D. Binnie ◽

Robert D. C. Elwes ◽

Charles E. Polkey ◽

...

Keyword(s):

Rem Sleep ◽

Predictive Value ◽

Scalp Eeg ◽

Epileptiform Discharges ◽

Interictal Epileptiform Discharges ◽

Eeg Recordings

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Automatic detection of generalized paroxysmal fast activity in Lennox-Gastaut syndrome using a bi-modal EEG time-frequency feature

10.1101/2019.12.28.19016089 ◽

2020 ◽

Author(s):

Amir Omidvarnia ◽

Aaron E.L. Warren ◽

Linda J. Dalic ◽

Mangor Pedersen ◽

John S. Archer ◽

...

Keyword(s):

Automatic Detection ◽

Time Frequency ◽

Scalp Eeg ◽

Epileptiform Discharges ◽

Fast Activity ◽

Brain Maps ◽

Detection Approach ◽

Interictal Eeg ◽

Eeg Recordings ◽

Frequency Feature

AbstractObjectiveMark-up of generalized interictal epileptiform discharges (IEDs) on EEG is an important step in the diagnosis and characterization of epilepsy. However, manual EEG mark-up is a time-consuming, subjective, and highly specialized task where the human reviewer needs to visually inspect a large amount of data to facilitate accurate clinical decisions. The objective of this study was to develop a framework for automated detection of generalized paroxysmal fast activity (GPFA), which is a characteristic type of generalized IED seen in scalp EEG recordings of patients with Lennox-Gastaut syndrome (LGS), a severe form of drug-resistant generalized epilepsy.MethodsWe studied 13 children with LGS who had GPFA events in their interictal EEG recordings. Time-frequency information derived from manually marked IEDs across multiple EEG channels was used to automatically detect similar events in each patient’s interictal EEG. We validated true positives and false positives of the proposed spike detection approach using both standalone scalp EEG and simultaneous EEG-functional MRI (EEG-fMRI) recordings.ResultsGPFA events displayed a consistent low-high frequency arrangement in the time-frequency domain. This ‘bi-modal’ spectral feature was most prominent over frontal EEG channels. Our automatic detection approach using this feature identified likely epileptic events with similar time-frequency properties to the manually marked GPFAs. Brain maps of EEG-fMRI signal change during these automatically detected IEDs were comparable to the EEG-fMRI brain maps derived from manual IED mark-up.ConclusionGPFA events have a characteristic bi-modal time-frequency feature that can be automatically detected from scalp EEG recordings in patients with LGS. Validity of this time-frequency feature is demonstrated by EEG-fMRI analysis of automatically detected events, which recapitulates the brain maps we have previously shown to underlie generalized IEDs in LGS.SignificanceThis study provides a novel methodology that paves the way for quick, automated, and objective inspection of generalized IEDs in LGS. The proposed framework may be extendable to a wider range of epilepsy syndromes in which monitoring the burden of epileptic activity can aid clinical decision-making. For example, automated quantification of generalized discharges may permit faster assessment of treatment response and estimation of future seizure risk.

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Detection of Intracranial Signatures of Interictal Epileptiform Discharges from Concurrent Scalp EEG

International Journal of Neural Systems ◽

10.1142/s0129065716500167 ◽

2016 ◽

Vol 26 (04) ◽

pp. 1650016 ◽

Cited By ~ 11

Author(s):

Loukianos Spyrou ◽

David Martín-Lopez ◽

Antonio Valentín ◽

Gonzalo Alarcón ◽

Saeid Sanei

Keyword(s):

Detection Algorithm ◽

The Other ◽

Intracranial Eeg ◽

Scalp Eeg ◽

Epileptiform Discharges ◽

Interictal Epileptiform Discharges ◽

Scalp Electroencephalogram ◽

Patients With Epilepsy ◽

Electrical Activities ◽

The Brain

Interictal epileptiform discharges (IEDs) are transient neural electrical activities that occur in the brain of patients with epilepsy. A problem with the inspection of IEDs from the scalp electroencephalogram (sEEG) is that for a subset of epileptic patients, there are no visually discernible IEDs on the scalp, rendering the above procedures ineffective, both for detection purposes and algorithm evaluation. On the other hand, intracranially placed electrodes yield a much higher incidence of visible IEDs as compared to concurrent scalp electrodes. In this work, we utilize concurrent scalp and intracranial EEG (iEEG) from a group of temporal lobe epilepsy (TLE) patients with low number of scalp-visible IEDs. The aim is to determine whether by considering the timing information of the IEDs from iEEG, the resulting concurrent sEEG contains enough information for the IEDs to be reliably distinguished from non-IED segments. We develop an automatic detection algorithm which is tested in a leave-subject-out fashion, where each test subject’s detection algorithm is based on the other patients’ data. The algorithm obtained a [Formula: see text] accuracy in recognizing scalp IED from non-IED segments with [Formula: see text] accuracy when trained and tested on the same subject. Also, it was able to identify nonscalp-visible IED events for most patients with a low number of false positive detections. Our results represent a proof of concept that IED information for TLE patients is contained in scalp EEG even if they are not visually identifiable and also that between subject differences in the IED topology and shape are small enough such that a generic algorithm can be used.

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Ictal scalp EEG in temporal lobe epilepsy with unitemporal versus bitemporal interictal epileptiform discharges

Neurology ◽

10.1212/wnl.45.5.889 ◽

1995 ◽

Vol 45 (5) ◽

pp. 889-896 ◽

Cited By ~ 102

Author(s):

B. J. Steinhoff ◽

N. K. So ◽

S. Lim ◽

H. O. Luders

Keyword(s):

Temporal Lobe Epilepsy ◽

Temporal Lobe ◽

Scalp Eeg ◽

Epileptiform Discharges ◽

Interictal Epileptiform Discharges

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Sleep spindles, ripples, and interictal epileptiform discharges in the human anterior and mediodorsal thalamus

10.1101/2021.08.12.456047 ◽

2021 ◽

Author(s):

Orsolya Szalardy ◽

Peter Simor ◽

Peter Przemyslaw Ujma ◽

Zsofia Jordan ◽

Laszlo Halasz ◽

...

Keyword(s):

Neural Plasticity ◽

Nrem Sleep ◽

Cortical Activity ◽

Sleep Spindles ◽

Time Frequency ◽

Scalp Eeg ◽

Epileptiform Discharges ◽

Interictal Epileptiform Discharges ◽

Human Thalamus ◽

Result Show

Sleep spindles are major oscillatory components of Non-Rapid Eye Movement (NREM) sleep, reflecting hyperpolarization-rebound sequences of thalamocortical neurons, the inhibition of which is caused by the NREM-dependent activation of GABAergic neurons in the reticular thalamic nucleus. Reports suggest a link between sleep spindles and several forms of interictal epileptic discharges (IEDs) which are considered as expressions of pathological off-line neural plasticity in the central nervous system. Here we investigated the relationship between thalamic sleep spindles, IEDs and ripples in the anterior and mediodorsal nuclei (ANT and MD) of epilepsy patients. Whole-night LFP from the ANT and MD were co-registered with scalp EEG/polysomnography by using externalized leads in 15 epilepsy patients undergoing Deep Brain Stimulation protocol. Slow (~12 Hz) and fast (~14 Hz) sleep spindles were present in the human ANT and MD. Roughly, one third of thalamic sleep spindles were associated with IEDs or ripples. Both IED- and ripple-associated spindles were longer than pure spindles. IED-associated thalamic sleep spindles were characterized by broadband increase in thalamic and cortical activity, both below and above the spindle frequency range, whereas ripple-associated thalamic spindles exceeded pure spindles in terms of 80-200 Hz thalamic, but not cortical activity as indicated by time-frequency analysis. These result show that thalamic spindles coupled with IEDs are reflected at the scalp slow and beta-gamma oscillation as well. IED density during sleep spindles in the MD, but not in the ANT was identified as correlates of years spent with epilepsy, whereas no signs of pathological processes were correlated with measures of ripple and spindle association. Furthermore, the density of ripple-associated sleep spindles in the ANT showed a positive correlation with general intelligence. Our findings indicate the complex and multifaceted role of the human thalamus in sleep spindle-related physiological and pathological neural plasticity.

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Automated Inter-Ictal Epileptiform Discharge Detection from Routine EEG

Healthier Lives, Digitally Enabled - Studies in Health Technology and Informatics ◽

10.3233/shti210012 ◽

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.

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Quantitative Measure to Differentiate Wicket Spike from Interictal Epileptiform Discharges

Communication in Biomathematical Sciences ◽

10.5614/cbms.2021.4.1.2 ◽

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.

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

10.1101/2021.07.29.21261253 ◽

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.

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Congruence and Discrepancy of Interictal and Ictal EEG With MRI Lesions in Pediatric Epilepsies

Clinical EEG and Neuroscience ◽

10.1177/1550059420921712 ◽

2020 ◽

Vol 51 (6) ◽

pp. 412-419

Author(s):

Jan Rémi ◽

Sophie Shen ◽

Moritz Tacke ◽

Philipp Probst ◽

Lucia Gerstl ◽

...

Keyword(s):

Pediatric Patients ◽

Pediatric Epilepsy ◽

Resonance Imaging ◽

Epileptiform Discharges ◽

Interictal Epileptiform Discharges ◽

Ictal Eeg ◽

Magnetic Resonance Imaging Mri ◽

Surface Eeg ◽

Frontal Lesions ◽

Video Eeg

Purpose. To evaluate the congruence or discrepancy of the localization of magnetic resonance imaging (MRI) lesions with interictal epileptiform discharges (IEDs) or epileptic seizure patterns (ESPs) in surface EEG in lesional pediatric epilepsy patients. Methods. We retrospectively analyzed presurgical MRI and video-EEG monitoring findings of patients up to age 18 years. Localization of MRI lesions were compared with ictal and interictal noninvasive EEG findings of patients with frontal, temporal, parietal, or occipital lesions. Results. A total of 71 patients were included. Localization of ESPs showed better congruence with MRI in patients with frontal lesions (n = 21, 77.5%) than in patients with temporal lesions (n = 24; 40.7%) ( P = .009). No significant IED distribution differences between MRI localizations could be found. Conclusions. MRI lesions and EEG findings are rarely fully congruent. Congruence of MRI lesions and ESPs was highest in children with frontal lesions. This is in contrast to adults, in whom temporal lesions showed the highest congruency with the EEG localization of ESP. Lesional pediatric patients should be acknowledged as surgical candidates despite incongruent findings of interictal and ictal surface EEG.

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