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EEG phase synchronization during absence seizures

Absence seizures—generalized rhythmic spike-and-wave discharges (SWDs) are the defining property of childhood (CAE) and juvenile (JAE) absence epilepsies. Such seizures are the most compelling examples of pathological neuronal hypersynchrony. All the absence detection algorithms proposed so far have...

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Autores principales: Glaba, Pawel, Latka, Miroslaw, Krause, Małgorzata J., Kroczka, Sławomir, Kuryło, Marta, Kaczorowska-Frontczak, Magdalena, Walas, Wojciech, Jernajczyk, Wojciech, Sebzda, Tadeusz, West, Bruce J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10317177/
https://www.ncbi.nlm.nih.gov/pubmed/37404335
http://dx.doi.org/10.3389/fninf.2023.1169584
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author Glaba, Pawel
Latka, Miroslaw
Krause, Małgorzata J.
Kroczka, Sławomir
Kuryło, Marta
Kaczorowska-Frontczak, Magdalena
Walas, Wojciech
Jernajczyk, Wojciech
Sebzda, Tadeusz
West, Bruce J.
author_facet Glaba, Pawel
Latka, Miroslaw
Krause, Małgorzata J.
Kroczka, Sławomir
Kuryło, Marta
Kaczorowska-Frontczak, Magdalena
Walas, Wojciech
Jernajczyk, Wojciech
Sebzda, Tadeusz
West, Bruce J.
author_sort Glaba, Pawel
collection PubMed
description Absence seizures—generalized rhythmic spike-and-wave discharges (SWDs) are the defining property of childhood (CAE) and juvenile (JAE) absence epilepsies. Such seizures are the most compelling examples of pathological neuronal hypersynchrony. All the absence detection algorithms proposed so far have been derived from the properties of individual SWDs. In this work, we investigate EEG phase synchronization in patients with CAE/JAE and healthy subjects to explore the possibility of using the wavelet phase synchronization index to detect seizures and quantify their disorganization (fragmentation). The overlap of the ictal and interictal probability density functions was high enough to preclude effective seizure detection based solely on changes in EEG synchronization. We used a machine learning classifier with the phase synchronization index (calculated for 1 s data segments with 0.5 s overlap) and the normalized amplitude as features to detect generalized SWDs. Using 19 channels (10-20 setup), we identified 99.2% of absences. However, the overlap of the segments classified as ictal with seizures was only 83%. The analysis showed that seizures were disorganized in approximately half of the 65 subjects. On average, generalized SWDs lasted about 80% of the duration of abnormal EEG activity. The disruption of the ictal rhythm can manifest itself as the disappearance of epileptic spikes (with high-amplitude delta waves persisting), transient cessation of epileptic discharges, or loss of global synchronization. The detector can analyze a real-time data stream. Its performance is good for a six-channel setup (Fp1, Fp2, F7, F8, O1, O2), which can be implemented as an unobtrusive EEG headband. False detections are rare for controls and young adults (0.03% and 0.02%, respectively). In patients, they are more frequent (0.5%), but in approximately 82% cases, classification errors are caused by short epileptiform discharges. Most importantly, the proposed detector can be applied to parts of EEG with abnormal EEG activity to quantitatively determine seizure fragmentation. This property is important because a previous study reported that the probability of disorganized discharges is eight times higher in JAE than in CAE. Future research must establish whether seizure properties (frequency, length, fragmentation, etc.) and clinical characteristics can help distinguish CAE and JAE.
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spelling pubmed-103171772023-07-04 EEG phase synchronization during absence seizures Glaba, Pawel Latka, Miroslaw Krause, Małgorzata J. Kroczka, Sławomir Kuryło, Marta Kaczorowska-Frontczak, Magdalena Walas, Wojciech Jernajczyk, Wojciech Sebzda, Tadeusz West, Bruce J. Front Neuroinform Neuroscience Absence seizures—generalized rhythmic spike-and-wave discharges (SWDs) are the defining property of childhood (CAE) and juvenile (JAE) absence epilepsies. Such seizures are the most compelling examples of pathological neuronal hypersynchrony. All the absence detection algorithms proposed so far have been derived from the properties of individual SWDs. In this work, we investigate EEG phase synchronization in patients with CAE/JAE and healthy subjects to explore the possibility of using the wavelet phase synchronization index to detect seizures and quantify their disorganization (fragmentation). The overlap of the ictal and interictal probability density functions was high enough to preclude effective seizure detection based solely on changes in EEG synchronization. We used a machine learning classifier with the phase synchronization index (calculated for 1 s data segments with 0.5 s overlap) and the normalized amplitude as features to detect generalized SWDs. Using 19 channels (10-20 setup), we identified 99.2% of absences. However, the overlap of the segments classified as ictal with seizures was only 83%. The analysis showed that seizures were disorganized in approximately half of the 65 subjects. On average, generalized SWDs lasted about 80% of the duration of abnormal EEG activity. The disruption of the ictal rhythm can manifest itself as the disappearance of epileptic spikes (with high-amplitude delta waves persisting), transient cessation of epileptic discharges, or loss of global synchronization. The detector can analyze a real-time data stream. Its performance is good for a six-channel setup (Fp1, Fp2, F7, F8, O1, O2), which can be implemented as an unobtrusive EEG headband. False detections are rare for controls and young adults (0.03% and 0.02%, respectively). In patients, they are more frequent (0.5%), but in approximately 82% cases, classification errors are caused by short epileptiform discharges. Most importantly, the proposed detector can be applied to parts of EEG with abnormal EEG activity to quantitatively determine seizure fragmentation. This property is important because a previous study reported that the probability of disorganized discharges is eight times higher in JAE than in CAE. Future research must establish whether seizure properties (frequency, length, fragmentation, etc.) and clinical characteristics can help distinguish CAE and JAE. Frontiers Media S.A. 2023-06-19 /pmc/articles/PMC10317177/ /pubmed/37404335 http://dx.doi.org/10.3389/fninf.2023.1169584 Text en Copyright © 2023 Glaba, Latka, Krause, Kroczka, Kuryło, Kaczorowska-Frontczak, Walas, Jernajczyk, Sebzda and West. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Glaba, Pawel
Latka, Miroslaw
Krause, Małgorzata J.
Kroczka, Sławomir
Kuryło, Marta
Kaczorowska-Frontczak, Magdalena
Walas, Wojciech
Jernajczyk, Wojciech
Sebzda, Tadeusz
West, Bruce J.
EEG phase synchronization during absence seizures
title EEG phase synchronization during absence seizures
title_full EEG phase synchronization during absence seizures
title_fullStr EEG phase synchronization during absence seizures
title_full_unstemmed EEG phase synchronization during absence seizures
title_short EEG phase synchronization during absence seizures
title_sort eeg phase synchronization during absence seizures
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10317177/
https://www.ncbi.nlm.nih.gov/pubmed/37404335
http://dx.doi.org/10.3389/fninf.2023.1169584
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