scholarly journals P.125 Discriminating sharp-wave ripples and interictal epileptiform discharges in patients with mesial temporal epilepsy using intracranial EEG recordings

2021 ◽  
Vol 48 (s3) ◽  
pp. S55-S55
Author(s):  
N Mortazavi ◽  
M Khaki ◽  
G Gilmore ◽  
J Burneo ◽  
D Steven ◽  
...  
Keyword(s):  
Temporal Frequency ◽  
University Hospital ◽  
Energy Distributions ◽  
Intracranial Eeg ◽  
Temporal Epilepsy ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Eeg Recordings ◽  
Different Characteristics ◽  
Temporal Frequency Characteristics

Background: Interictal epileptiform discharges (IEDs) are known as epilepsy biomarkers for seizure detection, and It is essential for clinicians to detect them from from physiological events with similar temporal frequency characteristics. Methods: We analyzed the SEEG recordings obtained from patients with medically-resistant epilepsy (MRE) implanted with DE at the Western University Hospital Epilepsy Unit. The data were cleaned, denoised, montaged and segmented based on the clinical annotations, such as sleep intervals and observed Ictals. For event detection, the signal waveform and its power were extracted symmetrically in non-overlapping intervals of 500 ms. Each waveform’s power across all detected spikes was computed and clustered based on their energy distributions. Results: The recordings included thirteen sessions of 24 hours of extracellular recordings from two patients, with 312 hours extracted from four hippocampus electrodes anterior and posterior hippocampus. Our results indicate IEDs carrying the most different characteristics in the bands [25-75] Hz; SWR, on the other hand, are distributed between [80-170] Hz. Conclusions: Our algorithm detected and successfully distinguished IED from SWRs based on their carrying energy during non-sleep periods. Also, the most powerful spectral features that they were distinguished from occur in [15-30] Hz and [75-90] Hz.

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

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.

scholarly journals Automated Detection of Interictal Epileptiform Discharges from Scalp Electroencephalograms by Convolutional Neural Networks

2020 ◽  
Vol 30 (11) ◽  
pp. 2050030 ◽  
Author(s):  
John Thomas ◽  
Jing Jin ◽  
Prasanth Thangavel ◽  
Elham Bagheri ◽  
Rajamanickam Yuvaraj ◽  
...  
Keyword(s):  
Neural Networks ◽  
South Carolina ◽  
Convolutional Neural Networks ◽  
Massachusetts General Hospital ◽  
Learning Curves ◽  
University Hospital ◽  
Superior Performance ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Eeg Recordings

Visual evaluation of electroencephalogram (EEG) for Interictal Epileptiform Discharges (IEDs) as distinctive biomarkers of epilepsy has various limitations, including time-consuming reviews, steep learning curves, interobserver variability, and the need for specialized experts. The development of an automated IED detector is necessary to provide a faster and reliable diagnosis of epilepsy. In this paper, we propose an automated IED detector based on Convolutional Neural Networks (CNNs). We have evaluated the proposed IED detector on a sizable database of 554 scalp EEG recordings (84 epileptic patients and 461 nonepileptic subjects) recorded at Massachusetts General Hospital (MGH), Boston. The proposed CNN IED detector has achieved superior performance in comparison with conventional methods with a mean cross-validation area under the precision–recall curve (AUPRC) of 0.838[Formula: see text]±[Formula: see text]0.040 and false detection rate of 0.2[Formula: see text]±[Formula: see text]0.11 per minute for a sensitivity of 80%. We demonstrated the proposed system to be noninferior to 30 neurologists on a dataset from the Medical University of South Carolina (MUSC). Further, we clinically validated the system at National University Hospital (NUH), Singapore, with an agreement accuracy of 81.41% with a clinical expert. Moreover, the proposed system can be applied to EEG recordings with any arbitrary number of channels.

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

2016 ◽  
Vol 26 (04) ◽  
pp. 1650016 ◽  
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.

scholarly journals Automated Inter-Ictal Epileptiform Discharge Detection from Routine EEG

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.

scholarly journals Quantitative Measure to Differentiate Wicket Spike from Interictal Epileptiform Discharges

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.

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

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.

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

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.

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

Epilepsia ◽  
1998 ◽  
Vol 39 (6) ◽  
pp. 628-632 ◽  
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

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

Seizure ◽  
2015 ◽  
Vol 29 ◽  
pp. 20-25 ◽  
Author(s):  
Konrad J. Werhahn ◽  
Elisabeth Hartl ◽  
Kristin Hamann ◽  
Markus Breimhorst ◽  
Soheyl Noachtar
Keyword(s):  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Eeg Recordings

How Long Should Routine EEG Be Recorded to Get Relevant Information?

2017 ◽  
Vol 49 (5) ◽  
pp. 335-341
Author(s):  
Hannah Doudoux ◽  
Kristina Skaare ◽  
Thomas Geay ◽  
Philippe Kahane ◽  
Jean L. Bosson ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Relevant Information ◽  
The Other ◽  
Neurologic Manifestation ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Eeg Abnormalities ◽  
Eeg Recordings ◽  
Time Required ◽  
Over Time

Objective. The optimal duration of routine EEG (rEEG) has not been determined on a clinical basis. This study aims to determine the time required to obtain relevant information during rEEG with respect to the clinical request. Method. All rEEGs performed over 3 months in unselected patients older than 14 years in an academic hospital were analyzed retrospectively. The latency required to obtain relevant information was determined for each rEEG by 2 independent readers blinded to the clinical data. EEG final diagnoses and latencies were analyzed with respect to the main clinical requests: subacute cognitive impairment, spells, transient focal neurologic manifestation or patients referred by epileptologists. Results. From 430 rEEGs performed in the targeted period, 364 were analyzed: 92% of the pathological rEEGs were provided within the first 10 minutes of recording. Slowing background activity was diagnosed from the beginning, whereas interictal epileptiform discharges were recorded over time. Moreover, the time elapsed to demonstrate a pattern differed significantly in the clinical groups: in patients with subacute cognitive impairment, EEG abnormalities appeared within the first 10 minutes, whereas in the other groups, data could be provided over time. Conclusion. Patients with subacute cognitive impairment differed from those in the other groups significantly in the elapsed time required to obtain relevant information during rEEG, suggesting that 10-minute EEG recordings could be sufficient, arguing in favor of individualized rEEG. However, this conclusion does not apply to intensive care unit patients.

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