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

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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Latency of interictal epileptiform discharges in long-term EEG recordings in epilepsy patients

Seizure ◽

10.1016/j.seizure.2015.03.012 ◽

2015 ◽

Vol 29 ◽

pp. 20-25 ◽

Cited By ~ 9

Author(s):

Konrad J. Werhahn ◽

Elisabeth Hartl ◽

Kristin Hamann ◽

Markus Breimhorst ◽

Soheyl Noachtar

Keyword(s):

Epileptiform Discharges ◽

Interictal Epileptiform Discharges ◽

Eeg Recordings

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Preoperative Electroencephalographic Investigation in Temporal Lobe Epilepsy: Extracranial and Intracranial Recordings

Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques ◽

10.1017/s0317167100032686 ◽

1991 ◽

Vol 18 (S4) ◽

pp. 554-558 ◽

Cited By ~ 30

Author(s):

Pierre Gloor

Keyword(s):

Temporal Lobe ◽

Video Monitoring ◽

Epileptiform Discharges ◽

Interictal Epileptiform Discharges ◽

Eeg Data ◽

Temporal Lobe Seizures ◽

Eeg Recordings ◽

Intracranial Recordings ◽

Eeg Features

ABSTRACT:Preoperative EEG investigations of patients with temporal lobe seizures include extracranial interictal and ictal recordings during wakefulness and sleep, including long-term EEG and video-monitoring. Interictal epileptiform discharges when evaluated conservatively and in conjunction with other EEG and non-EEG localizing information, provide valuable guidance for the identification of the area to be resected, as do ictal recordings. When extracranial EEG features in conjunction with non-EEG data provide conflicting localizing information, intracranial recordings with stereotaxically implanted depth and epidural electrodes are used. Intracranial recordings must be designed to avoid biasing the exploration strategy in favor of one's preferred localizing hypothesis. Patients with evidence for bitemporal epileptogenic dysfunction in extracranial EEG recordings are suitable candidates for intracranial recordings. The majority of the patients explored in this manner show that all or more than 80% of their seizures arise from one temporal lobe. Excision of that lobe yields satisfactory results in a fair proportion of these patients. The number of satisfactory outcomes is, however, still somewhat less than in patients with unilateral temporal foci in extracranial 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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The first-hour-of-the-day sleep EEG reliably identifies interictal epileptiform discharges during long-term video-EEG monitoring

Seizure ◽

10.1016/j.seizure.2018.10.015 ◽

2018 ◽

Vol 63 ◽

pp. 48-51 ◽

Cited By ~ 2

Author(s):

Xi Liu ◽

Naoum P. Issa ◽

Sandra Rose ◽

Shasha Wu ◽

Taixin Sun ◽

...

Keyword(s):

Sleep Eeg ◽

Eeg Monitoring ◽

Epileptiform Discharges ◽

Interictal Epileptiform Discharges ◽

Video Eeg

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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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Distributed Brain Co-Processor for Neurophysiologic Tracking and Adaptive Stimulation: Application to Drug Resistant Epilepsy

10.1101/2021.03.08.434476 ◽

2021 ◽

Author(s):

Vladimir Sladky ◽

Petr Nejedly ◽

Filip Mivalt ◽

Benjamin H. Brinkmann ◽

Inyong Kim ◽

...

Keyword(s):

Data Storage ◽

Brain Stimulation ◽

Open Loop ◽

Natural Environments ◽

Drug Resistant ◽

Embedded Computing ◽

Epileptiform Discharges ◽

Interictal Epileptiform Discharges ◽

Drug Resistant Epilepsy

AbstractElectrical brain stimulation is a proven therapy for epilepsy, but long-term seizure free outcomes are rare. Early implantable devices were developed for open-loop stimulation without sensing, embedded computing or adaptive therapy. Recent device advances include sensing and closed-loop responsive stimulation, but these clinically available devices lack adequate computing, data storage and patient interface to concisely catalog behavior, seizures, and brain electrophysiology, despite the critical importance of these details for epilepsy management. Here we describe the first application of a distributed brain co-processor providing an intuitive, bi-directional interface between device implant, patient & physician, and implement it in human and canine patients with epilepsy living in their natural environments. Automated behavioral state tracking (awake and sleep) and electrophysiologic classifiers for interictal epileptiform discharges and electrographic seizures are run on local hand-held and distributed cloud computing resources to guide adaptive electrical stimulation. These algorithms were first developed and parameterized using long-term retrospective data from 10 humans and 11 canines with epilepsy and then implemented prospectively in two pet canines and one human with drug resistant epilepsy as they naturally navigate their lives in society.One Sentence SummaryWe created a distributed brain co-processor for continuous neurophysiologic tracking and controlling adaptive brain stimulation to treat epilepsy.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.239-240.921 ◽

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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