1
|
Aud'hui M, Kachenoura A, Yochum M, Kaminska A, Nabbout R, Wendling F, Kuchenbuch M, Benquet P. Detection of seizure onset in childhood absence epilepsy. Clin Neurophysiol 2024; 163:267-279. [PMID: 38644110 DOI: 10.1016/j.clinph.2024.03.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 03/19/2024] [Accepted: 03/26/2024] [Indexed: 04/23/2024]
Abstract
OBJECTIVE This study aims to detect the seizure onset, in childhood absence epilepsy, as early as possible. Indeed, interfering with absence seizures with sensory simulation has been shown to be possible on the condition that the stimulation occurs soon enough after the seizure onset. METHODS We present four variations (two supervised, two unsupervised) of an algorithm designed to detect the onset of absence seizures from 4 scalp electrodes, and compare their performance with that of a state-of-the-art algorithm. We exploit the characteristic shape of spike-wave discharges to detect the seizure onset. Their performance is assessed on clinical electroencephalograms from 63 patients with confirmed childhood absence epilepsy. RESULTS The proposed approaches succeed in early detection of the seizure onset, contrary to the classical detection algorithm. Indeed, the results clearly show the superiority of the proposed methods for small delays of detection, under 750 ms from the onset. CONCLUSION The performance of the proposed unsupervised methods is equivalent to that of the supervised ones. The use of only four electrodes makes the pipeline suitable to be embedded in a wearable device. SIGNIFICANCE The proposed pipelines perform early detection of absence seizures, which constitutes a prerequisite for a closed-loop system.
Collapse
Affiliation(s)
- M Aud'hui
- Univ Rennes, INSERM, LTSI - UMR 1099, Rennes F-35000, France
| | - A Kachenoura
- Univ Rennes, INSERM, LTSI - UMR 1099, Rennes F-35000, France
| | - M Yochum
- Univ Rennes, INSERM, LTSI - UMR 1099, Rennes F-35000, France.
| | - A Kaminska
- Department of Clinical Neurophysiology, Hôpital Necker Enfants Malades, AP-HP, Université de Paris, Paris, France
| | - R Nabbout
- Department of Clinical Neurophysiology, Hôpital Necker Enfants Malades, AP-HP, Université de Paris, Paris, France; Reference Center for Rare Epilepsies, Department of Pediatric Neurology, Member of EPICARE Network, Institute Imagine INSERM 1163, Université de Paris, Paris, France
| | - F Wendling
- Univ Rennes, INSERM, LTSI - UMR 1099, Rennes F-35000, France
| | - M Kuchenbuch
- Pediatric and Genetic Department, CHU, Nancy, France
| | - P Benquet
- Univ Rennes, INSERM, LTSI - UMR 1099, Rennes F-35000, France
| |
Collapse
|
2
|
Japaridze G, Loeckx D, Buckinx T, Armand Larsen S, Proost R, Jansen K, MacMullin P, Paiva N, Kasradze S, Rotenberg A, Lagae L, Beniczky S. Automated detection of absence seizures using a wearable electroencephalographic device: a phase 3 validation study and feasibility of automated behavioral testing. Epilepsia 2023; 64 Suppl 4:S40-S46. [PMID: 35176173 DOI: 10.1111/epi.17200] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 02/01/2022] [Accepted: 02/14/2022] [Indexed: 11/03/2022]
Abstract
OBJECTIVE Our primary goal was to measure the accuracy of fully automated absence seizure detection, using a wearable electroencephalographic (EEG) device. As a secondary goal, we also tested the feasibility of automated behavioral testing triggered by the automated detection. METHODS We conducted a phase 3 clinical trial (NCT04615442), with a prospective, multicenter, blinded study design. The input was the one-channel EEG recorded with dry electrodes embedded into a wearable headband device connected to a smartphone. The seizure detection algorithm was developed using artificial intelligence (convolutional neural networks). During the study, the predefined algorithm, with predefined cutoff value, analyzed the EEG in real time. The gold standard was derived from expert evaluation of simultaneously recorded full-array video-EEGs. In addition, we evaluated the patients' responsiveness to the automated alarms on the smartphone, and we compared it with the behavioral changes observed in the clinical video-EEGs. RESULTS We recorded 102 consecutive patients (57 female, median age = 10 years) on suspicion of absence seizures. We recorded 364 absence seizures in 39 patients. Device deficiency was 4.67%, with a total recording time of 309 h. Average sensitivity per patient was 78.83% (95% confidence interval [CI] = 69.56%-88.11%), and median sensitivity was 92.90% (interquartile range [IQR] = 66.7%-100%). The average false detection rate was .53/h (95% CI = .32-.74). Most patients (n = 66, 64.71%) did not have any false alarms. The median F1 score per patient was .823 (IQR = .57-1). For the total recording duration, F1 score was .74. We assessed the feasibility of automated behavioral testing in 36 seizures; it correctly documented nonresponsiveness in 30 absence seizures, and responsiveness in six electrographic seizures. SIGNIFICANCE Automated detection of absence seizures with a wearable device will improve seizure quantification and will promote assessment of patients in their home environment. Linking automated seizure detection to automated behavioral testing will provide valuable information from wearable devices.
Collapse
Affiliation(s)
| | | | | | - Sidsel Armand Larsen
- Department of Clinical Neurophysiology, Danish Epilepsy Center Filadelfia, Dianalund, Denmark
| | | | | | - Paul MacMullin
- Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Natalia Paiva
- Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Sofia Kasradze
- Institute of Neurology and Neuropsychology, Tbilisi, Georgia
| | - Alexander Rotenberg
- Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Center Filadelfia, Dianalund, Denmark
- Department of Clinical Neurophysiology, Aarhus University Hospital, and Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| |
Collapse
|
3
|
Sugden RJ, Pham-Kim-Nghiem-Phu VLL, Campbell I, Leon A, Diamandis P. Remote collection of electrophysiological data with brain wearables: opportunities and challenges. Bioelectron Med 2023; 9:12. [PMID: 37340487 DOI: 10.1186/s42234-023-00114-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 05/30/2023] [Indexed: 06/22/2023] Open
Abstract
Collection of electroencephalographic (EEG) data provides an opportunity to non-invasively study human brain plasticity, learning and the evolution of various neuropsychiatric disorders. Traditionally, due to sophisticated hardware, EEG studies have been largely limited to research centers which restrict both testing contexts and repeated longitudinal measures. The emergence of low-cost "wearable" EEG devices now provides the prospect of frequent and remote monitoring of the human brain for a variety of physiological and pathological brain states. In this manuscript, we survey evidence that EEG wearables provide high-quality data and review various software used for remote data collection. We then discuss the growing body of evidence supporting the feasibility of remote and longitudinal EEG data collection using wearables including a discussion of potential biomedical applications of these protocols. Lastly, we discuss some additional challenges needed for EEG wearable research to gain further widespread adoption.
Collapse
Affiliation(s)
- Richard James Sugden
- Department of Medical Biophysics, University of Toronto, Toronto, ON, M5S 1A8, Canada
- Princess Margaret Cancer Center, University Health Network, 610 University Avenue, Toronto, ON, M5G 2C1, Canada
| | | | - Ingrid Campbell
- Princess Margaret Cancer Center, University Health Network, 610 University Avenue, Toronto, ON, M5G 2C1, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Alberto Leon
- Princess Margaret Cancer Center, University Health Network, 610 University Avenue, Toronto, ON, M5G 2C1, Canada
| | - Phedias Diamandis
- Department of Medical Biophysics, University of Toronto, Toronto, ON, M5S 1A8, Canada.
- Laboratory Medicine Program, University Health Network, 200 Elizabeth Street, Toronto, ON, M5G 2C4, Canada.
| |
Collapse
|
4
|
Glaba P, Latka M, Krause MJ, Kroczka S, Kuryło M, Kaczorowska-Frontczak M, Walas W, Jernajczyk W, Sebzda T, West BJ. EEG phase synchronization during absence seizures. Front Neuroinform 2023; 17:1169584. [PMID: 37404335 PMCID: PMC10317177 DOI: 10.3389/fninf.2023.1169584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 05/25/2023] [Indexed: 07/06/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Pawel Glaba
- Department of Biomedical Engineering, Wroclaw University of Science and Technology, Wrocław, Poland
| | - Miroslaw Latka
- Department of Biomedical Engineering, Wroclaw University of Science and Technology, Wrocław, Poland
| | | | - Sławomir Kroczka
- Department of Child Neurology, Jagiellonian University Medical College, Kraków, Poland
| | - Marta Kuryło
- Department of Pediatric Neurology, T. Marciniak Hospital, Wrocław, Poland
| | | | - Wojciech Walas
- Department of Anesthesiology, Intensive Care and Regional Extracorporeal Membrane Oxygenation (ECMO) Center, Institute of Medical Sciences, University of Opole, Opole, Poland
| | - Wojciech Jernajczyk
- Clinical Neurophysiology, Institute of Psychiatry and Neurology, Warszawa, Poland
| | - Tadeusz Sebzda
- Department of Physiology and Pathophysiology, Medical University of Wroclaw, Wrocław, Poland
| | - Bruce J. West
- Center for Nonlinear Science, University of North Texas, Denton, TX, United States
| |
Collapse
|
5
|
EEG Markers of Treatment Resistance in Idiopathic Generalized Epilepsy: From Standard EEG Findings to Advanced Signal Analysis. Biomedicines 2022; 10:biomedicines10102428. [PMID: 36289690 PMCID: PMC9598660 DOI: 10.3390/biomedicines10102428] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 09/20/2022] [Accepted: 09/23/2022] [Indexed: 12/02/2022] Open
Abstract
Idiopathic generalized epilepsy (IGE) represents a common form of epilepsy in both adult and pediatric epilepsy units. Although IGE has been long considered a relatively benign epilepsy syndrome, a remarkable proportion of patients could be refractory to treatment. While some clinical prognostic factors have been largely validated among IGE patients, the impact of routine electroencephalography (EEG) findings in predicting drug resistance is still controversial and a growing number of authors highlighted the potential importance of capturing the sleep state in this setting. In addition, the development of advanced computational techniques to analyze EEG data has opened new opportunities in the identification of reliable and reproducible biomarkers of drug resistance in IGE patients. In this manuscript, we summarize the EEG findings associated with treatment resistance in IGE by reviewing the results of studies considering standard EEGs, 24-h EEG recordings, and resting-state protocols. We discuss the role of 24-h EEG recordings in assessing seizure recurrence in light of the potential prognostic relevance of generalized fast discharges occurring during sleep. In addition, we highlight new and promising biomarkers as identified by advanced EEG analysis, including hypothesis-driven functional connectivity measures of background activity and data-driven quantitative findings revealed by machine learning approaches. Finally, we thoroughly discuss the methodological limitations observed in existing studies and briefly outline future directions to identify reliable and replicable EEG biomarkers in IGE patients.
Collapse
|