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Liao J, Wang J, Zhan CA, Yang F. Parameterized aperiodic and periodic components of single-channel EEG enables reliable seizure detection. Phys Eng Sci Med 2024; 47:31-47. [PMID: 37747646 DOI: 10.1007/s13246-023-01340-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 09/14/2023] [Indexed: 09/26/2023]
Abstract
Although it is clinically important, a reliable and economical solution to automatic seizure detection for patients at home is yet to be developed. Traditional algorithms rely on multi-channel EEG signals and features of canonical EEG power description. This study is aimed to propose an effective single-channel EEG seizure detection method centered on novel EEG power parameterization and channel selection algorithms. We employed the publicly available multi-channel CHB-MIT Scalp EEG database to gauge the effectiveness of our approach. We first adapted a power spectra parameterization algorithm to characterize the aperiodic and periodic components of the ictal and inter-ictal EEGs. We selected four features based on their statistical significance and interpretability, and developed a ranking approach to channel selection for each patient. We then tested the effectiveness of our approaches to channel and feature selection for automatic seizure detection using support vector machine (SVM) as the classifier. The performance of our algorithm was evaluated using five-fold cross-validation and compared to those methods of comparable complexity (using one or two channels of EEG), in terms of accuracy, specificity, sensitivity, precision and F1 score. Some channels of EEG signals show strikingly different distributions of PSD features between the ictal and inter-ictal states. Four features including the offset and exponent parameters for the aperiodic component and the first and second highest total power (TPW1 and TPW2) form the basis of channel selection and the input of SVM classifier. The selected channel is found to be patient-specific. Our approach has achieved a mean sensitivity of 95.6%, specificity of 99.2%, accuracy of 98.6%, precision of 95.5%, and F1 score of 95.5%. Compared with algorithms in previous studies that used one or two channels of EEG signals, ours outperforms in specificity and accuracy with comparable sensitivity. EEG power spectra parameterization to feature extraction and feature ranking-based channel selection are found to enable efficient and effective automatic seizure detection based on single-channel EEG signal.
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Affiliation(s)
- Jiahui Liao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Jun Wang
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Chang'an A Zhan
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China.
| | - Feng Yang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.
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2
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Ji H, Xu T, Xue T, Xu T, Yan Z, Liu Y, Chen B, Jiang W. An effective fusion model for seizure prediction: GAMRNN. Front Neurosci 2023; 17:1246995. [PMID: 37674519 PMCID: PMC10477703 DOI: 10.3389/fnins.2023.1246995] [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: 06/25/2023] [Accepted: 07/24/2023] [Indexed: 09/08/2023] Open
Abstract
The early prediction of epileptic seizures holds paramount significance in patient care and medical research. Extracting useful spatial-temporal features to facilitate seizure prediction represents a primary challenge in this field. This study proposes GAMRNN, a novel methodology integrating a dual-layer gated recurrent unit (GRU) model with a convolutional attention module. GAMRNN aims to capture intricate spatial-temporal characteristics by highlighting informative feature channels and spatial pattern dynamics. We employ the Lion optimization algorithm to enhance the model's generalization capability and predictive accuracy. Our evaluation of GAMRNN on the widely utilized CHB-MIT EEG dataset demonstrates its effectiveness in seizure prediction. The results include an impressive average classification accuracy of 91.73%, sensitivity of 88.09%, specificity of 92.09%, and a low false positive rate of 0.053/h. Notably, GAMRNN enables early seizure prediction with a lead time ranging from 5 to 35 min, exhibiting remarkable performance improvements compared to similar prediction models.
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Affiliation(s)
- Hong Ji
- Shaanxi Provincial Key Laboratory of Fashion Design Intelligence, Xi'an Polytechnic University, Xi'an, China
| | - Ting Xu
- Shaanxi Provincial Key Laboratory of Fashion Design Intelligence, Xi'an Polytechnic University, Xi'an, China
| | - Tao Xue
- Shaanxi Provincial Key Laboratory of Fashion Design Intelligence, Xi'an Polytechnic University, Xi'an, China
| | - Tao Xu
- School of Software, Northwestern Polytechnical University, Xi'an, China
| | - Zhiqiang Yan
- Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Yonghong Liu
- Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Badong Chen
- Institute of Artistic Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China
| | - Wen Jiang
- Xijing Hospital, Fourth Military Medical University, Xi'an, China
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3
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Mathew J, Sivakumaran N, Karthick PA. Automated Detection of Seizure Types from the Higher-Order Moments of Maximal Overlap Wavelet Distribution. Diagnostics (Basel) 2023; 13:621. [PMID: 36832108 PMCID: PMC9955002 DOI: 10.3390/diagnostics13040621] [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: 12/31/2022] [Revised: 01/29/2023] [Accepted: 02/02/2023] [Indexed: 02/11/2023] Open
Abstract
In this work, an attempt has been made to develop an automated system for detecting electroclinical seizures such as tonic-clonic seizures, complex partial seizures, and electrographic seizures (EGSZ) using higher-order moments of scalp electroencephalography (EEG). The scalp EEGs of the publicly available Temple University database are utilized in this study. The higher-order moments, namely skewness and kurtosis, are extracted from the temporal, spectral, and maximal overlap wavelet distributions of EEG. The features are computed from overlapping and non-overlapping moving windowing functions. The results show that the wavelet and spectral skewness of EEG is higher in EGSZ than in other types. All the extracted features are found to have significant differences (p < 0.05), except for temporal kurtosis and skewness. A support vector machine with a radial basis kernel designed using maximal overlap wavelet skewness yields a maximum accuracy of 87%. In order to improve the performance, the Bayesian optimization technique is utilized to determine the suitable kernel parameters. The optimized model achieves the highest accuracy of 96% and an MCC of 91% in three-class classification. The study is found to be promising, and it could facilitate the rapid identification process of life-threatening seizures.
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Affiliation(s)
- Joseph Mathew
- Physiological Measurements and Instrumentation Laboratory, Department of Instrumentation and Control Engineering, National Institute of Technology, Tiruchirappalli 620015, Tamil Nadu, India
- Department of Applied Electronics and Instrumentation Engineering, Rajagiri School of Engineering and Technology, Cochin 682039, Kerala, India
| | - Natarajan Sivakumaran
- Physiological Measurements and Instrumentation Laboratory, Department of Instrumentation and Control Engineering, National Institute of Technology, Tiruchirappalli 620015, Tamil Nadu, India
| | - P. A. Karthick
- Physiological Measurements and Instrumentation Laboratory, Department of Instrumentation and Control Engineering, National Institute of Technology, Tiruchirappalli 620015, Tamil Nadu, India
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4
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El-Gindy SAE, Ibrahim FE, Alabasy M, Abdelzaher HM, El-Refy M, Khalaf AAM, El-Dolil SM, El-Fishawy AS, Taha TE, El-Rabaie ESM, Dessouky MI, El-Dokany I, Oraby OA, N. Alotaiby T, Alshebeili SA, Abd El-Samie FE. Detection of Abnormal Activities from Various Signals Based on Statistical Analysis. WIRELESS PERSONAL COMMUNICATIONS 2022; 125:1013-1046. [DOI: 10.1007/s11277-022-09565-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/29/2022] [Indexed: 09/02/2023]
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5
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Panagiotopoulou M, Papasavvas CA, Schroeder GM, Thomas RH, Taylor PN, Wang Y. Fluctuations in EEG band power at subject-specific timescales over minutes to days explain changes in seizure evolutions. Hum Brain Mapp 2022; 43:2460-2477. [PMID: 35119173 PMCID: PMC9057101 DOI: 10.1002/hbm.25796] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 12/30/2021] [Accepted: 01/23/2022] [Indexed: 01/14/2023] Open
Abstract
Epilepsy is recognised as a dynamic disease, where both seizure susceptibility and seizure characteristics themselves change over time. Specifically, we recently quantified the variable electrographic spatio-temporal seizure evolutions that exist within individual patients. This variability appears to follow subject-specific circadian, or longer, timescale modulations. It is therefore important to know whether continuously recorded interictaliEEG features can capture signatures of these modulations over different timescales. In this study, we analyse continuous intracranial electroencephalographic (iEEG) recordings from video-telemetry units and find fluctuations in iEEG band power over timescales ranging from minutes up to 12 days. As expected and in agreement with previous studies, we find that all subjects show a circadian fluctuation in their iEEG band power. We additionally detect other fluctuations of similar magnitude on subject-specific timescales. Importantly, we find that a combination of these fluctuations on different timescales can explain changes in seizure evolutions in most subjects above chance level. These results suggest that subject-specific fluctuations in iEEG band power over timescales of minutes to days may serve as markers of seizure modulating processes. We hope that future study can link these detected fluctuations to their biological driver(s). There is a critical need to better understand seizure modulating processes, as this will enable the development of novel treatment strategies that could minimise the seizure spread, duration or severity and therefore the clinical impact of seizures.
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Affiliation(s)
- Mariella Panagiotopoulou
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems GroupSchool of Computing, Newcastle UniversityNewcastle upon Tyne
| | - Christoforos A. Papasavvas
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems GroupSchool of Computing, Newcastle UniversityNewcastle upon Tyne
| | - Gabrielle M. Schroeder
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems GroupSchool of Computing, Newcastle UniversityNewcastle upon Tyne
| | - Rhys H. Thomas
- Faculty of Medical SciencesNewcastle UniversityNewcastle upon Tyne
| | - Peter N. Taylor
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems GroupSchool of Computing, Newcastle UniversityNewcastle upon Tyne
- Faculty of Medical SciencesNewcastle UniversityNewcastle upon Tyne
- UCL Queen Square Institute of Neurology, Queen SquareLondon
| | - Yujiang Wang
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems GroupSchool of Computing, Newcastle UniversityNewcastle upon Tyne
- Faculty of Medical SciencesNewcastle UniversityNewcastle upon Tyne
- UCL Queen Square Institute of Neurology, Queen SquareLondon
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6
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Ong JS, Wong SN, Arulsamy A, Watterson JL, Shaikh MF. Medical Technology: A Systematic Review on Medical Devices Utilized for Epilepsy Prediction and Management. Curr Neuropharmacol 2022; 20:950-964. [PMID: 34749622 PMCID: PMC9881104 DOI: 10.2174/1570159x19666211108153001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 10/30/2021] [Accepted: 11/03/2021] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Epilepsy is a devastating neurological disorder that affects nearly 70 million people worldwide. Epilepsy causes uncontrollable, unprovoked and unpredictable seizures that reduce the quality of life of those afflicted, with 1-9 epileptic patient deaths per 1000 patients occurring annually due to sudden unexpected death in epilepsy (SUDEP). Predicting the onset of seizures and managing them may help patients from harming themselves and may improve their well-being. For a long time, electroencephalography (EEG) devices have been the mainstay for seizure detection and monitoring. This systematic review aimed to elucidate and critically evaluate the latest advancements in medical devices, besides EEG, that have been proposed for the management and prediction of epileptic seizures. A literature search was performed on three databases, PubMed, Scopus and EMBASE. METHODS Following title/abstract screening by two independent reviewers, 27 articles were selected for critical analysis in this review. RESULTS These articles revealed ambulatory, non-invasive and wearable medical devices, such as the in-ear EEG devices; the accelerometer-based devices and the subcutaneous implanted EEG devices might be more acceptable than traditional EEG systems. In addition, extracerebral signalbased devices may be more efficient than EEG-based systems, especially when combined with an intervention trigger. Although further studies may still be required to improve and validate these proposed systems before commercialization, these findings may give hope to epileptic patients, particularly those with refractory epilepsy, to predict and manage their seizures. CONCLUSION The use of medical devices for epilepsy may improve patients' independence and quality of life and possibly prevent sudden unexpected death in epilepsy (SUDEP).
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Affiliation(s)
- Jen Sze Ong
- Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway, Selangor, Malaysia
| | - Shuet Nee Wong
- School of Medicine, Queen’s University Belfast, Belfast, United Kingdom
| | - Alina Arulsamy
- Neuropharmacology Research Laboratory, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway, Selangor, Malaysia
| | - Jessica L. Watterson
- Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway, Selangor, Malaysia
| | - Mohd. Farooq Shaikh
- Neuropharmacology Research Laboratory, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway, Selangor, Malaysia,Address correspondence to this author at the Neuropharmacology Research Laboratory, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Selangor, Malaysia; Tel/Fax: +60 3 5514 4483; E-mail:
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7
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Sanz-García A, Perez-Romero M, Ortega GJ. Spectral and network characterization of focal seizure types and phases. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 217:106704. [PMID: 35220198 DOI: 10.1016/j.cmpb.2022.106704] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 01/27/2022] [Accepted: 02/19/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Currently, epileptic seizure characterization relies on several clinical features that allow their classification into different types. The present work aims to characterize both seizure types and phases based exclusively on electrophysiological characteristics. METHODS Based on the analysis of intracranial EEG recordings of 129 seizures from 22 patients obtained from the European Epilepsy Database, network and spectral measures were calculated in five-second temporal windows. Statistically significant differences between each window of the seizure phases (preictal, ictal, and postictal) and the interictal phase were used to identify/classify seizure types and their phases. A support vector machine (SVM) working on a multidimensional feature space of network and spectral measures was implemented for the classification of each seizure type; a traditional statistical approach was also conducted to highlight the underlying patterns to each seizure type or phase. RESULTS The percentage of correct classification of seizure types, corrected by chance, provided by the SVM exceeded 70%, considering all measures and the entire seizure (preictal + ictal + postictal). This percentage increased to more than 80% when all the measures during the ictal period for the depth electrodes or during the postictal for subdural electrodes were considered. Regarding the statistical approach, several measures presented a monotonic ascending and descending behavior with respect to seizure severity; these changes were observed during the ictal and postictal periods. Some measures were specific of each seizure type. CONCLUSIONS Our results provide a new framework to seizure characterization and reveal the possibility of an exclusively intracranial EEG-based classification. This could be used to build an automatic seizure classification system and provides new evidence of the network-related physiopathology of epilepsies. Thus, the novelty of this work is the possibility of differentiating seizure types based exclusively on the EEG recordings, providing evidence of the underlying patterns or characteristics to each seizure type and/or phase that would allow their optimal classification.
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Affiliation(s)
- Ancor Sanz-García
- Instituto de Investigación Sanitaria, Hospital Universitario de la Princesa, Diego de León 62, 9th floor, Madrid 28006, Spain.
| | - Miriam Perez-Romero
- Instituto de Investigación Sanitaria, Hospital Universitario de la Princesa, Diego de León 62, 9th floor, Madrid 28006, Spain
| | - Guillermo J Ortega
- Instituto de Investigación Sanitaria, Hospital Universitario de la Princesa, Diego de León 62, 9th floor, Madrid 28006, Spain; CONICET, National Scientific and Technical Research Council, Argentina; Universidad Nacional de Quilmes, Science and Technology Department, Argentina
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8
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Mathew J, Ramakrishnan Manuskandan S, Sivakumaran N, Karthick PA. Detection of Tonic-Clonic Seizures using Wavelet Entropy of Scalp EEG. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2423-2426. [PMID: 34891770 DOI: 10.1109/embc46164.2021.9630664] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Epilepsy is the most common chronic neurologic disorder characterized by the recurrence of unprovoked seizures. These seizures are paroxysmal events that result from abnormal neuronal discharges and are categorized into various types based on the clinical manifestations and localization. Tonic-Clonic seizures (TCSZ) may lead to injuries, and constitute the major risk factor for sudden unexpected death in epilepsy (SUDEP), especially in unattended patients. Therapeutic decisions and clinical trials rely on Video EEG which is not practical outside of clinical setting. In this study, wavelet entropy of scalp EEG signals are utilized to discriminate the seizures with and without clinical manifestations. The scalp EEG records from the publically available Temple University Hospital (TUH) dataset are considered for this work. A sevenlevel, fourth order Daubechies (db4) wavelet is utilized for the decomposition of first four seconds of scalp EEG during seizures. The entropy is extracted from the resultant coefficients and are used to develop SVM based models. Most of the extracted features found to have significant differences (p<0.05). The results show that polynomial SVM model achieves an accuracy of 95.5%, positive predictive value (PPV) of 99.4%, negative predictive value (NPV) of 91.57% and F-Score of 95.9%. Therefore, the proposed approach could be a support in detecting life-threatening seizures.
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9
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El-Gindy SAE, Hamad A, El-Shafai W, Khalaf AAM, El-Dolil SM, Taha TE, El-Fishawy AS, Alotaiby TN, Alshebeili SA, El-Samie FEA. Efficient communication and EEG signal classification in wavelet domain for epilepsy patients. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021; 12:9193-9208. [DOI: 10.1007/s12652-020-02624-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 10/20/2020] [Indexed: 09/01/2023]
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10
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Sinha N, Peternell N, Schroeder GM, de Tisi J, Vos SB, Winston GP, Duncan JS, Wang Y, Taylor PN. Focal to bilateral tonic-clonic seizures are associated with widespread network abnormality in temporal lobe epilepsy. Epilepsia 2021; 62:729-741. [PMID: 33476430 PMCID: PMC8600951 DOI: 10.1111/epi.16819] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 12/28/2020] [Accepted: 12/28/2020] [Indexed: 12/16/2022]
Abstract
OBJECTIVE Our objective was to identify whether the whole-brain structural network alterations in patients with temporal lobe epilepsy (TLE) and focal to bilateral tonic-clonic seizures (FBTCS) differ from alterations in patients without FBTCS. METHODS We dichotomized a cohort of 83 drug-resistant patients with TLE into those with and without FBTCS and compared each group to 29 healthy controls. For each subject, we used diffusion-weighted magnetic resonance imaging to construct whole-brain structural networks. First, we measured the extent of alterations by performing FBTCS-negative (FBTCS-) versus control and FBTCS-positive (FBTCS+) versus control comparisons, thereby delineating altered subnetworks of the whole-brain structural network. Second, by standardizing each patient's networks using control networks, we measured the subject-specific abnormality at every brain region in the network, thereby quantifying the spatial localization and the amount of abnormality in every patient. RESULTS Both FBTCS+ and FBTCS- patient groups had altered subnetworks with reduced fractional anisotropy and increased mean diffusivity compared to controls. The altered subnetwork in FBTCS+ patients was more widespread than in FBTCS- patients (441 connections altered at t > 3, p < .001 in FBTCS+ compared to 21 connections altered at t > 3, p = .01 in FBTCS-). Significantly greater abnormalities-aggregated over the entire brain network as well as assessed at the resolution of individual brain areas-were present in FBTCS+ patients (p < .001, d = .82, 95% confidence interval = .32-1.3). In contrast, the fewer abnormalities present in FBTCS- patients were mainly localized to the temporal and frontal areas. SIGNIFICANCE The whole-brain structural network is altered to a greater and more widespread extent in patients with TLE and FBTCS. We suggest that these abnormal networks may serve as an underlying structural basis or consequence of the greater seizure spread observed in FBTCS.
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Affiliation(s)
- Nishant Sinha
- Faculty of Medical Sciences, Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, UK.,Computational Neuroscience, Neurology, and Psychiatry Lab, Interdisciplinary Computing and Complex BioSystems Research Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK
| | - Natalie Peternell
- Computational Neuroscience, Neurology, and Psychiatry Lab, Interdisciplinary Computing and Complex BioSystems Research Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK
| | - Gabrielle M Schroeder
- Computational Neuroscience, Neurology, and Psychiatry Lab, Interdisciplinary Computing and Complex BioSystems Research Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK
| | - Jane de Tisi
- National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - Sjoerd B Vos
- National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London Queen Square Institute of Neurology, London, UK.,Centre for Medical Image Computing, University College London, London, UK.,Neuroradiological Academic Unit, University College London Queen Square Institute of Neurology, University College London, London, UK
| | - Gavin P Winston
- National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London Queen Square Institute of Neurology, London, UK.,Epilepsy Society MRI Unit, Chalfont St Peter, UK.,Division of Neurology, Department of Medicine, Queen's University, Kingston, ON, Canada
| | - John S Duncan
- National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London Queen Square Institute of Neurology, London, UK.,Epilepsy Society MRI Unit, Chalfont St Peter, UK
| | - Yujiang Wang
- Computational Neuroscience, Neurology, and Psychiatry Lab, Interdisciplinary Computing and Complex BioSystems Research Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK.,National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - Peter N Taylor
- Computational Neuroscience, Neurology, and Psychiatry Lab, Interdisciplinary Computing and Complex BioSystems Research Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK.,National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London Queen Square Institute of Neurology, London, UK
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11
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Schroeder GM, Diehl B, Chowdhury FA, Duncan JS, de Tisi J, Trevelyan AJ, Forsyth R, Jackson A, Taylor PN, Wang Y. Seizure pathways change on circadian and slower timescales in individual patients with focal epilepsy. Proc Natl Acad Sci U S A 2020; 117:11048-11058. [PMID: 32366665 PMCID: PMC7245106 DOI: 10.1073/pnas.1922084117] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Personalized medicine requires that treatments adapt to not only the patient but also changing factors within each individual. Although epilepsy is a dynamic disorder characterized by pathological fluctuations in brain state, surprisingly little is known about whether and how seizures vary in the same patient. We quantitatively compared within-patient seizure network evolutions using intracranial electroencephalographic (iEEG) recordings of over 500 seizures from 31 patients with focal epilepsy (mean 16.5 seizures per patient). In all patients, we found variability in seizure paths through the space of possible network dynamics. Seizures with similar pathways tended to occur closer together in time, and a simple model suggested that seizure pathways change on circadian and/or slower timescales in the majority of patients. These temporal relationships occurred independent of whether the patient underwent antiepileptic medication reduction. Our results suggest that various modulatory processes, operating at different timescales, shape within-patient seizure evolutions, leading to variable seizure pathways that may require tailored treatment approaches.
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Affiliation(s)
- Gabrielle M Schroeder
- Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, NE4 5TG, United Kingdom
| | - Beate Diehl
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, United Kingdom
| | - Fahmida A Chowdhury
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, United Kingdom
| | - John S Duncan
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, United Kingdom
| | - Jane de Tisi
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, United Kingdom
| | - Andrew J Trevelyan
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| | - Rob Forsyth
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| | - Andrew Jackson
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| | - Peter N Taylor
- Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, NE4 5TG, United Kingdom
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, United Kingdom
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| | - Yujiang Wang
- Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, NE4 5TG, United Kingdom;
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, United Kingdom
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
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12
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Could we have missed out the seizure onset: A study based on intracranial EEG. Clin Neurophysiol 2020; 131:114-126. [DOI: 10.1016/j.clinph.2019.10.011] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 09/25/2019] [Accepted: 10/10/2019] [Indexed: 11/20/2022]
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13
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Schönberger J, Birk N, Lachner-Piza D, Dümpelmann M, Schulze-Bonhage A, Jacobs J. High-frequency oscillations mirror severity of human temporal lobe seizures. Ann Clin Transl Neurol 2019; 6:2479-2488. [PMID: 31750633 PMCID: PMC6917313 DOI: 10.1002/acn3.50941] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 10/18/2019] [Indexed: 02/06/2023] Open
Abstract
Objective Many patients with epilepsy have both focal and bilateral tonic‐clonic seizures (BTCSs), but it is largely unclear why ictal activity spreads only sometimes. Previous work indicates that interictal high‐frequency oscillations (HFOs), traditionally subdivided into ripples (80–250 Hz) and fast ripples (250–500 Hz), are a promising biomarker of epileptogenicity. We aimed to investigate whether HFOs correlate with the emergence of seizure activity and whether they differ between focal seizures (FSs) with impaired awareness and BTCSs. Methods We retrospectively analyzed 15 FSs and 13 BTCSs from seven patients with mesial temporal lobe epilepsy, each of them with at least one BTCS and at least one FS. Representative intervals of intracranial electroencephalography from the seizure onset zone (SOZ) and remote non‐SOZ areas were selected to compare pre‐ictal, complex focal, tonic‐clonic, and postictal periods. Ripples and fast ripples were visually identified and their density, that is, percentage of time occupied by the respective events, computed. Results Ripple and fast ripple densities increased inside the SOZ after seizure onset (P < 0.01) and in remote areas after progression to BTCSs (P < 0.01). Postictal SOZ ripple density dropped below pre‐ictal levels (P < 0.001). Prior to onset of bilateral tonic‐clonic movements, ripple density inside the SOZ is higher in BTCSs than in FSs (P < 0.05). Interpretation Ripples and fast ripples correlate with onset and spread of ictal activity. Abundant ripples inside the SOZ may reflect the activation of specific neuronal networks related to imminent spread of seizure activity.
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Affiliation(s)
- Jan Schönberger
- Universitätsklinikum Freiburg, Epilepsiezentrum, Breisacher Straße 64, 79106, Freiburg im Breisgau, Germany.,Klinik für Neuropädiatrie und Muskelerkrankungen, Universitätsklinikum Freiburg, Mathildenstraße 1, 79106, Freiburg im Breisgau, Germany.,Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Nadja Birk
- Universitätsklinikum Freiburg, Epilepsiezentrum, Breisacher Straße 64, 79106, Freiburg im Breisgau, Germany.,Klinik für Neuropädiatrie und Muskelerkrankungen, Universitätsklinikum Freiburg, Mathildenstraße 1, 79106, Freiburg im Breisgau, Germany
| | - Daniel Lachner-Piza
- Universitätsklinikum Freiburg, Epilepsiezentrum, Breisacher Straße 64, 79106, Freiburg im Breisgau, Germany
| | - Matthias Dümpelmann
- Universitätsklinikum Freiburg, Epilepsiezentrum, Breisacher Straße 64, 79106, Freiburg im Breisgau, Germany
| | - Andreas Schulze-Bonhage
- Universitätsklinikum Freiburg, Epilepsiezentrum, Breisacher Straße 64, 79106, Freiburg im Breisgau, Germany
| | - Julia Jacobs
- Universitätsklinikum Freiburg, Epilepsiezentrum, Breisacher Straße 64, 79106, Freiburg im Breisgau, Germany.,Klinik für Neuropädiatrie und Muskelerkrankungen, Universitätsklinikum Freiburg, Mathildenstraße 1, 79106, Freiburg im Breisgau, Germany
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Pizarro D, Ilyas A, Chaitanya G, Toth E, Irannejad A, Romeo A, Riley KO, Iasemidis L, Pati S. Spectral organization of focal seizures within the thalamotemporal network. Ann Clin Transl Neurol 2019; 6:1836-1848. [PMID: 31468745 PMCID: PMC6764631 DOI: 10.1002/acn3.50880] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 08/02/2019] [Accepted: 08/06/2019] [Indexed: 01/08/2023] Open
Abstract
Objective To investigate dynamic changes in neural activity between the anterior nucleus of the thalamus (ANT) and the seizure onset zone (SOZ) in patients with drug‐resistant temporal lobe epilepsy (TLE) based on anatomic location, seizure subtype, and state of vigilance (SOV). Methods Eleven patients undergoing stereoelectroencephalography for seizure localization were recruited prospectively for local field potential (LFP) recording directly from the ANT. The SOZ was identified using line length and epileptogenicity index. Changes in power spectral density (PSD) were compared between the two anatomic sites as seizures (N = 53) transitioned from interictal baseline to the posttermination stage. Results At baseline, the thalamic LFPs were significantly lower and distinct from the SOZ with the presence of higher power in the fast ripple band (P < 0.001). Temporal changes in ictal power of neural activity within ANT mimic those of the SOZ, are increased significantly at seizure onset (P < 0.05), and are distinct for seizures that impaired awareness or that secondarily generalized (P < 0.05). The onset of seizure was preceded by a decrease in the mean power spectral density (PSD) in ANT and SOZ (P < 0.05). Neural activity correlated with different states of vigilance at seizure onset within the ANT but not in the SOZ (P = 0.005). Interpretation The ANT can be recruited at the onset of mesial temporal lobe seizures, and the recruitment pattern differs with seizure subtypes. Furthermore, changes in neural dynamics precede seizure onset and are widespread to involve temporo‐thalamic regions, thereby providing an opportunity to intervene early with closed‐loop DBS.
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Affiliation(s)
- Diana Pizarro
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama.,Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, Alabama
| | - Adeel Ilyas
- Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, Alabama.,Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, Alabama
| | - Ganne Chaitanya
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama.,Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, Alabama
| | - Emilia Toth
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama.,Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, Alabama
| | - Auriana Irannejad
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama.,Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, Alabama
| | - Andrew Romeo
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, Alabama
| | - Kristen O Riley
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, Alabama
| | - Leonidas Iasemidis
- Center for Biomedical Engineering and Rehabilitation Science, Louisiana Tech University, Ruston, Louisiana
| | - Sandipan Pati
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama.,Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, Alabama
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