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Loube DK, Tan YL, Yoshii-Contreras J, Kleen J, Rao VR, Chang EF, Knowlton RC. Ictal EEG Source Imaging With Supplemental Electrodes. J Clin Neurophysiol 2023:00004691-990000000-00102. [PMID: 37820169 DOI: 10.1097/wnp.0000000000001025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2023] Open
Abstract
INTRODUCTION Noninvasive brain imaging tests play a major role in guiding decision-making and the usage of invasive, costly intracranial electroencephalogram (ICEEG) in the presurgical epilepsy evaluation. This study prospectively examined the concordance in localization between ictal EEG source imaging (ESI) and ICEEG as a reference standard. METHODS Between August 2014 and April 2019, patients during video monitoring with scalp EEG were screened for those with intractable focal epilepsy believed to be amenable to surgical treatment. Additional 10-10 electrodes (total = 31-38 per patient, "31+") were placed over suspected regions of seizure onset in 104 patients. Of 42 patients requiring ICEEG, 30 (mean age 30, range 19-59) had sufficiently localized subsequent intracranial studies to allow comparison of localization between tests. ESI was performed using realistic forward boundary element models used in dipole and distributed source analyses. RESULTS At least partial sublobar concordance between ESI and ICEEG solutions was obtained in 97% of cases, with 73% achieving complete agreement. Median Euclidean distances between ESI and ICEEG solutions ranged from 25 to 30 mm (dipole) and 23 to 38 mm (distributed source). The latter was significantly more accurate with 31+ compared with 21 electrodes (P < 0.01). A difference of ≤25 mm was present in two thirds of the cases. No significant difference was found between dipole and distributed source analyses. CONCLUSIONS A practical method of ictal ESI (nonuniform placement of 31-38 electrodes) yields high accuracy for seizure localization in epilepsy surgery candidates. These results support routine clinical application of ESI in the presurgical evaluation.
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Affiliation(s)
| | - Yee-Leng Tan
- Department of Neurology, National Neuroscience Institute, SingHealth, Republic of Singapore
| | - June Yoshii-Contreras
- Division of Epilepsy, Department of Neurology, University of California San Diego, California, U.S.A; and
| | - Jonathan Kleen
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, U.S.A
| | - Vikram R Rao
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, U.S.A
| | - Edward F Chang
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, U.S.A
| | - Robert C Knowlton
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, U.S.A
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Gorur K, Olmez E, Ozer Z, Cetin O. EEG-Driven Biometric Authentication for Investigation of Fourier Synchrosqueezed Transform-ICA Robust Framework. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023. [DOI: 10.1007/s13369-023-07798-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
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de Borman A, Vespa S, Absil PA, El Tahry R. Estimation of seizure onset zone from ictal scalp EEG using independent component analysis in extratemporal lobe epilepsy. J Neural Eng 2022; 19. [PMID: 35172295 DOI: 10.1088/1741-2552/ac55ad] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 02/16/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The purpose of this study is to localize the seizure onset zone of patients suffering from drug-resistant epilepsy. During the last two decades, multiple studies proposed the use of Independent Component Analysis (ICA) to analyze ictal electroencephalogram (EEG) recordings. This study aims at evaluating ICA potential with quantitative measurements. In particular, we address the challenging step where the components extracted by ICA of an ictal nature must be selected. APPROACH We considered a cohort of 10 patients suffering from extratemporal lobe epilepsy who were rendered seizure-free after surgery. Different sets of pre-processing parameters were compared and component features were explored to help distinguish ictal components from others. Quantitative measurements were implemented to determine whether some of the components returned by ICA were located within the resection zone and thus likely to be ictal. Finally, an assistance to the component selection was proposed based on the implemented features. MAIN RESULTS For every seizure, at least one component returned by ICA was localized within the resection zone, with the optimal pre-processing parameters. Three features were found to distinguish components localized within the resection zone: the dispersion of their active brain sources, the ictal rhythm power and the contribution to the EEG variance. Using the implemented component selection assistance based on the features, the probability that the first proposed component yields an accurate estimation reaches 51.43% (without assistance: 24.74%). The accuracy reaches 80% when considering the best result within the first five components. SIGNIFICANCE This study confirms the utility of ICA for ictal EEG analysis in extratemporal lobe epilepsy, and suggests relevant features to analyze the components returned by ICA. A component selection assistance is proposed to guide clinicians in their choice for ictal components.
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Affiliation(s)
- Aurélie de Borman
- ICTEAM, Université catholique de Louvain, Avenue Georges Lemaitre 4, Louvain-la-Neuve, 1348, BELGIUM
| | - Simone Vespa
- Institute of Neuroscience (IoNS), Université catholique de Louvain, Avenue Mounier 53 bte B1.53.02, Louvain-la-Neuve, 1348, BELGIUM
| | - Pierre-Antoine Absil
- ICTEAM, Université catholique de Louvain, Avenue Georges Lemaître 4 bte L4.05.01, Louvain-la-Neuve, 1348, BELGIUM
| | - Riëm El Tahry
- Institute of Neuroscience (IoNS), Université catholique de Louvain, Avenue Mounier 53 bte B1.53.02, Louvain-la-Neuve, 1348, BELGIUM
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Carboni M, Brunet D, Seeber M, Michel CM, Vulliemoz S, Vorderwülbecke BJ. Linear distributed inverse solutions for interictal EEG source localisation. Clin Neurophysiol 2021; 133:58-67. [PMID: 34801964 DOI: 10.1016/j.clinph.2021.10.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 09/27/2021] [Accepted: 10/09/2021] [Indexed: 01/09/2023]
Abstract
OBJECTIVE To compare the spatial accuracy of 6 linear distributed inverse solutions for EEG source localisation of interictal epileptic discharges: Minimum Norm, Weighted Minimum Norm, Low-Resolution Electromagnetic Tomography (LORETA), Local Autoregressive Average (LAURA), Standardised LORETA, and Exact LORETA. METHODS Spatial accuracy was assessed clinically by retrospectively comparing the maximum source of averaged interictal discharges to the resected brain area in 30 patients with successful epilepsy surgery, based on 204-channel EEG. Additionally, localisation errors of the inverse solutions were assessed in computer simulations, with different levels of noise added to the signal in both sensor space and source space. RESULTS In the clinical evaluations, the source maximum was located inside the resected brain area in 50-57% of patients when using LORETA or LAURA, while all other inverse solutions performed significantly worse (17-30%; corrected p < 0.01). In the simulation studies, when noise levels exceeded 10%, LORETA and LAURA had substantially smaller localisation errors than the other inverse solutions. CONCLUSIONS LORETA and LAURA provided the highest spatial accuracy both in clinical and simulated data, alongside with a comparably high robustness towards noise. SIGNIFICANCE Among the different linear inverse solution algorithms tested, LORETA and LAURA might be preferred for interictal EEG source localisation.
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Affiliation(s)
- Margherita Carboni
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland; Functional Brain Mapping Lab, Department of Basic Neurosciences, University of Geneva, Campus Biotech, 9 Chemin des Mines, 1202 Geneva, Switzerland
| | - Denis Brunet
- Functional Brain Mapping Lab, Department of Basic Neurosciences, University of Geneva, Campus Biotech, 9 Chemin des Mines, 1202 Geneva, Switzerland
| | - Martin Seeber
- Functional Brain Mapping Lab, Department of Basic Neurosciences, University of Geneva, Campus Biotech, 9 Chemin des Mines, 1202 Geneva, Switzerland
| | - Christoph M Michel
- Functional Brain Mapping Lab, Department of Basic Neurosciences, University of Geneva, Campus Biotech, 9 Chemin des Mines, 1202 Geneva, Switzerland
| | - Serge Vulliemoz
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland
| | - Bernd J Vorderwülbecke
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland; Epilepsy-Center Berlin-Brandenburg, Department of Neurology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany.
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Saute RL, Peixoto-Santos JE, Velasco TR, Leite JP. Improving surgical outcome with electric source imaging and high field magnetic resonance imaging. Seizure 2021; 90:145-154. [PMID: 33608134 DOI: 10.1016/j.seizure.2021.02.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/26/2021] [Accepted: 02/04/2021] [Indexed: 12/14/2022] Open
Abstract
While most patients with focal epilepsy present with clear structural abnormalities on standard, 1.5 or 3 T MRI, some patients are MRI-negative. For those, quantitative MRI techniques, such as volumetry, voxel-based morphometry, and relaxation time measurements can aid in finding the epileptogenic focus. High-field MRI, just recently approved for clinical use by the FDA, increases the resolution and, in several publications, was shown to improve the detection of focal cortical dysplasias and mild cortical malformations. For those cases without any tissue abnormality in neuroimaging, even at 7 T, scalp EEG alone is insufficient to delimitate the epileptogenic zone. They may benefit from the use of high-density EEG, in which the increased number of electrodes helps improve spatial sampling. The spatial resolution of even low-density EEG can benefit from electric source imaging techniques, which map the source of the recorded abnormal activity, such as interictal epileptiform discharges, focal slowing, and ictal rhythm. These EEG techniques help localize the irritative, functional deficit, and seizure-onset zone, to better estimate the epileptogenic zone. Combining those technologies allows several drug-resistant cases to be submitted to surgery, increasing the odds of seizure freedom and providing a must needed hope for patients with epilepsy.
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Affiliation(s)
- Ricardo Lutzky Saute
- Department of Neurosciences and Behavioral Sciences, Ribeirao Preto Medical School, University of Sao Paulo, Brazil
| | - Jose Eduardo Peixoto-Santos
- Discipline of Neuroscience, Department of Neurology and Neurosurgery, Paulista School of Medicine, Unifesp, Brazil
| | - Tonicarlo R Velasco
- Department of Neurosciences and Behavioral Sciences, Ribeirao Preto Medical School, University of Sao Paulo, Brazil
| | - Joao Pereira Leite
- Department of Neurosciences and Behavioral Sciences, Ribeirao Preto Medical School, University of Sao Paulo, Brazil.
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Wang X, Zhang Y, Yu B, Salhi A, Chen R, Wang L, Liu Z. Prediction of protein-protein interaction sites through eXtreme gradient boosting with kernel principal component analysis. Comput Biol Med 2021; 134:104516. [PMID: 34119922 DOI: 10.1016/j.compbiomed.2021.104516] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 05/24/2021] [Accepted: 05/24/2021] [Indexed: 12/22/2022]
Abstract
Predicting protein-protein interaction sites (PPI sites) can provide important clues for understanding biological activity. Using machine learning to predict PPI sites can mitigate the cost of running expensive and time-consuming biological experiments. Here we propose PPISP-XGBoost, a novel PPI sites prediction method based on eXtreme gradient boosting (XGBoost). First, the characteristic information of protein is extracted through the pseudo-position specific scoring matrix (PsePSSM), pseudo-amino acid composition (PseAAC), hydropathy index and solvent accessible surface area (ASA) under the sliding window. Next, these raw features are preprocessed to obtain more optimal representations in order to achieve better prediction. In particular, the synthetic minority oversampling technique (SMOTE) is used to circumvent class imbalance, and the kernel principal component analysis (KPCA) is applied to remove redundant characteristics. Finally, these optimal features are fed to the XGBoost classifier to identify PPI sites. Using PPISP-XGBoost, the prediction accuracy on the training dataset Dset186 reaches 85.4%, and the accuracy on the independent validation datasets Dtestset72, PDBtestset164, Dset_448 and Dset_355 reaches 85.3%, 83.9%, 85.8% and 85.4%, respectively, which all show an increase in accuracy against existing PPI sites prediction methods. These results demonstrate that the PPISP-XGBoost method can further enhance the prediction of PPI sites.
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Affiliation(s)
- Xue Wang
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China
| | - Yaqun Zhang
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China
| | - Bin Yu
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China; Key Laboratory of Computational Science and Application of Hainan Province, Haikou, 571158, China.
| | - Adil Salhi
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia
| | - Ruixin Chen
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China
| | - Lin Wang
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China
| | - Zengfeng Liu
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China
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Çınar S. Design of an automatic hybrid system for removal of eye-blink artifacts from EEG recordings. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102543] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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van Mierlo P, Vorderwülbecke BJ, Staljanssens W, Seeck M, Vulliémoz S. Ictal EEG source localization in focal epilepsy: Review and future perspectives. Clin Neurophysiol 2020; 131:2600-2616. [PMID: 32927216 DOI: 10.1016/j.clinph.2020.08.001] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 07/12/2020] [Accepted: 08/04/2020] [Indexed: 11/25/2022]
Abstract
Electroencephalographic (EEG) source imaging localizes the generators of neural activity in the brain. During presurgical epilepsy evaluation, EEG source imaging of interictal epileptiform discharges is an established tool to estimate the irritative zone. However, the origin of interictal activity can be partly or fully discordant with the origin of seizures. Therefore, source imaging based on ictal EEG data to determine the seizure onset zone can provide precious clinical information. In this descriptive review, we address the importance of localizing the seizure onset zone based on noninvasive EEG recordings as a complementary analysis that might reduce the burden of the presurgical evaluation. We identify three major challenges (low signal-to-noise ratio of the ictal EEG data, spread of ictal activity in the brain, and validation of the developed methods) and discuss practical solutions. We provide an extensive overview of the existing clinical studies to illustrate the potential clinical utility of EEG-based localization of the seizure onset zone. Finally, we conclude with future perspectives and the needs for translating ictal EEG source imaging into clinical practice.
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Affiliation(s)
- Pieter van Mierlo
- Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University, Corneel Heymanslaan 10, 9000 Ghent, Belgium.
| | - Bernd J Vorderwülbecke
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine Geneva, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland; Department of Neurology, Epilepsy-Center Berlin-Brandenburg, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany.
| | - Willeke Staljanssens
- Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University, Corneel Heymanslaan 10, 9000 Ghent, Belgium
| | - Margitta Seeck
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine Geneva, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland.
| | - Serge Vulliémoz
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine Geneva, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland.
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Khosropanah P, Ho ETW, Lim KS, Fong SL, Thuy Le MA, Narayanan V. EEG Source Imaging (ESI) utility in clinical practice. BIOMED ENG-BIOMED TE 2020; 65:/j/bmte.ahead-of-print/bmt-2019-0128/bmt-2019-0128.xml. [PMID: 32623371 DOI: 10.1515/bmt-2019-0128] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 02/21/2020] [Indexed: 11/15/2022]
Abstract
Epilepsy surgery is an important treatment modality for medically refractory focal epilepsy. The outcome of surgery usually depends on the localization accuracy of the epileptogenic zone (EZ) during pre-surgical evaluation. Good localization can be achieved with various electrophysiological and neuroimaging approaches. However, each approach has its own merits and limitations. Electroencephalography (EEG) Source Imaging (ESI) is an emerging model-based computational technique to localize cortical sources of electrical activity within the brain volume, three-dimensionally. ESI based pre-surgical evaluation gives an overall clinical yield of 73-91%, depending on choice of head model, inverse solution and EEG electrode density. It is a cost effective, non-invasive method which provides valuable additional information in presurgical evaluation due to its high localizing value specifically in MRI-negative cases, extra or basal temporal lobe epilepsy, multifocal lesions such as tuberous sclerosis or cases with multiple hypotheses. Unfortunately, less than 1% of surgical centers in developing countries use this method as a part of pre-surgical evaluation. This review promotes ESI as a useful clinical tool especially for patients with lesion-negative MRI to determine EZ cost-effectively with high accuracy under the optimized conditions.
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Affiliation(s)
- Pegah Khosropanah
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
- Division of Neurology, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Eric Tatt-Wei Ho
- Center for Intelligent Signal & Imaging Research, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia
- Department of Electrical & Electronics Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia
| | - Kheng-Seang Lim
- Division of Neurology, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Si-Lei Fong
- Division of Neurology, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Minh-An Thuy Le
- Division of Neurology, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
- Department of Neurology, Faculty of Medicine, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Viet Nam
| | - Vairavan Narayanan
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
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