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

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2021.104287 ◽

2021 ◽

pp. 104287

Author(s):

Amir Omidvarnia ◽

Aaron E.L. Warren ◽

Linda J. Dalic ◽

Mangor Pedersen ◽

Graeme Jackson

Keyword(s):

Frequency Analysis ◽

Automatic Detection ◽

Time Frequency Analysis ◽

Time Frequency ◽

Fast Activity ◽

Interictal Eeg

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

Data in Brief ◽

10.1016/j.dib.2021.107680 ◽

2021 ◽

Vol 39 ◽

pp. 107680

Author(s):

Fasil OK ◽

Rajesh R ◽

Rajith K. Ravindren

Keyword(s):

Automatic Detection ◽

Pediatric Epilepsy ◽

Scalp Eeg ◽

Epileptiform Discharges ◽

Interictal Epileptiform Discharges ◽

Eeg Recordings

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

Scientific Reports ◽

10.1038/s41598-021-83337-3 ◽

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.

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High similarity between EEG from subcutaneous and proximate scalp electrodes in patients with temporal lobe epilepsy

Journal of Neurophysiology ◽

10.1152/jn.00320.2018 ◽

2018 ◽

Vol 120 (3) ◽

pp. 1451-1460 ◽

Cited By ~ 13

Author(s):

Sigge Weisdorf ◽

Sirin W. Gangstad ◽

Jonas Duun-Henriksen ◽

Karina S. S. Mosholt ◽

Troels W. Kjær

Keyword(s):

Temporal Lobe Epilepsy ◽

Temporal Lobe ◽

Temporal Dynamics ◽

Real Life ◽

High Similarity ◽

Time Frequency ◽

Scalp Eeg ◽

Eeg Recordings ◽

The Impact

Subcutaneous recording using electroencephalography (EEG) has the potential to enable ultra-long-term epilepsy monitoring in real-life conditions because it allows the patient increased mobility and discreteness. This study is the first to compare physiological and epileptiform EEG signals from subcutaneous and scalp EEG recordings in epilepsy patients. Four patients with probable or definite temporal lobe epilepsy were monitored with simultaneous scalp and subcutaneous EEG recordings. EEG recordings were compared by correlation and time-frequency analysis across an array of clinically relevant waveforms and patterns. We found high similarity between the subcutaneous EEG channels and nearby temporal scalp channels for most investigated electroencephalographic events. In particular, the temporal dynamics of one typical temporal lobe seizure in one patient were similar in scalp and subcutaneous recordings in regard to frequency distribution and morphology. Signal similarity is strongly related to the distance between the subcutaneous and scalp electrodes. On the basis of these limited data, we conclude that subcutaneous EEG recordings are very similar to scalp recordings in both time and time-frequency domains, if the distance between them is small. As many electroencephalographic events are local/regional, the positioning of the subcutaneous electrodes should be considered carefully to reflect the relevant clinical question. The impact of implantation depth of the subcutaneous electrode on recording quality should be investigated further. NEW & NOTEWORTHY This study is the first publication comparing the detection of clinically relevant, pathological EEG features from a subcutaneous recording system designed for out-patient ultra-long-term use to gold standard scalp EEG recordings. Our study shows that subcutaneous channels are very similar to comparable scalp channels, but also point out some issues yet to be resolved.

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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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Validation of an Automatic Arousal Detection Algorithm for Whole-Night Sleep EEG Recordings

Clocks & Sleep ◽

10.3390/clockssleep2030020 ◽

2020 ◽

Vol 2 (3) ◽

pp. 258-272

Author(s):

Daphne Chylinski ◽

Franziska Rudzik ◽

Dorothée Coppieters ‘t Wallant ◽

Martin Grignard ◽

Nora Vandeleene ◽

...

Keyword(s):

Visual Detection ◽

Sleep Stage ◽

Automatic Detection ◽

Detection Algorithm ◽

Human Detection ◽

Sleep Eeg ◽

Older Individuals ◽

Time Frequency ◽

Eeg Recordings ◽

Research Centres

Arousals during sleep are transient accelerations of the EEG signal, considered to reflect sleep perturbations associated with poorer sleep quality. They are typically detected by visual inspection, which is time consuming, subjective, and prevents good comparability across scorers, studies and research centres. We developed a fully automatic algorithm which aims at detecting artefact and arousal events in whole-night EEG recordings, based on time-frequency analysis with adapted thresholds derived from individual data. We ran an automated detection of arousals over 35 sleep EEG recordings in healthy young and older individuals and compared it against human visual detection from two research centres with the aim to evaluate the algorithm performance. Comparison across human scorers revealed a high variability in the number of detected arousals, which was always lower than the number detected automatically. Despite indexing more events, automatic detection showed high agreement with human detection as reflected by its correlation with human raters and very good Cohen’s kappa values. Finally, the sex of participants and sleep stage did not influence performance, while age may impact automatic detection, depending on the human rater considered as gold standard. We propose our freely available algorithm as a reliable and time-sparing alternative to visual detection of arousals.

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