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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| Format: | Article |
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Frontiers Media S.A.
2023-06-01
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| Series: | Frontiers in Neuroinformatics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fninf.2023.1169584/full |
| _version_ | 1797800898505736192 |
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| author | Pawel Glaba Miroslaw Latka Małgorzata J. Krause Sławomir Kroczka Marta Kuryło Magdalena Kaczorowska-Frontczak Wojciech Walas Wojciech Jernajczyk Tadeusz Sebzda Bruce J. West |
| author_facet | Pawel Glaba Miroslaw Latka Małgorzata J. Krause Sławomir Kroczka Marta Kuryło Magdalena Kaczorowska-Frontczak Wojciech Walas Wojciech Jernajczyk Tadeusz Sebzda Bruce J. West |
| author_sort | Pawel Glaba |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-13T04:42:11Z |
| format | Article |
| id | doaj.art-36a7a044a0434bc0a109fbbe700018d6 |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-03-13T04:42:11Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-36a7a044a0434bc0a109fbbe700018d62023-06-19T05:06:15ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962023-06-011710.3389/fninf.2023.11695841169584EEG phase synchronization during absence seizuresPawel Glaba0Miroslaw Latka1Małgorzata J. Krause2Sławomir Kroczka3Marta Kuryło4Magdalena Kaczorowska-Frontczak5Wojciech Walas6Wojciech Jernajczyk7Tadeusz Sebzda8Bruce J. West9Department of Biomedical Engineering, Wroclaw University of Science and Technology, Wrocław, PolandDepartment of Biomedical Engineering, Wroclaw University of Science and Technology, Wrocław, PolandDepartment of Pediatric Neurology, T. Marciniak Hospital, Wrocław, PolandDepartment of Child Neurology, Jagiellonian University Medical College, Kraków, PolandDepartment of Pediatric Neurology, T. Marciniak Hospital, Wrocław, PolandThe Children's Memorial Health Institute, Warszawa, PolandDepartment of Anesthesiology, Intensive Care and Regional Extracorporeal Membrane Oxygenation (ECMO) Center, Institute of Medical Sciences, University of Opole, Opole, PolandClinical Neurophysiology, Institute of Psychiatry and Neurology, Warszawa, PolandDepartment of Physiology and Pathophysiology, Medical University of Wroclaw, Wrocław, PolandCenter for Nonlinear Science, University of North Texas, Denton, TX, United StatesAbsence 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.https://www.frontiersin.org/articles/10.3389/fninf.2023.1169584/fullepilepsyabsence seizuresynchronizationwaveletsseizure detectionchildhood absence epilepsy |
| spellingShingle | Pawel Glaba Miroslaw Latka Małgorzata J. Krause Sławomir Kroczka Marta Kuryło Magdalena Kaczorowska-Frontczak Wojciech Walas Wojciech Jernajczyk Tadeusz Sebzda Bruce J. West EEG phase synchronization during absence seizures Frontiers in Neuroinformatics epilepsy absence seizure synchronization wavelets seizure detection childhood absence epilepsy |
| 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 | epilepsy absence seizure synchronization wavelets seizure detection childhood absence epilepsy |
| url | https://www.frontiersin.org/articles/10.3389/fninf.2023.1169584/full |
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