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Miao Y, Iimura Y, Sugano H, Fukumori K, Tanaka T. Seizure onset zone identification using phase-amplitude coupling and multiple machine learning approaches for interictal electrocorticogram. Cogn Neurodyn 2023; 17:1591-1607. [PMID: 37969944 PMCID: PMC10640557 DOI: 10.1007/s11571-022-09915-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 10/26/2022] [Accepted: 11/17/2022] [Indexed: 12/12/2022] Open
Abstract
Automatic seizure onset zone (SOZ) localization using interictal electrocorticogram (ECoG) improves the diagnosis and treatment of patients with medically refractory epilepsy. This study aimed to investigate the characteristics of phase-amplitude coupling (PAC) extracted from interictal ECoG and the feasibility of PAC serving as a promising biomarker for SOZ identification. We employed the mean vector length modulation index approach on the 20-s ECoG window to calculate PAC features between low-frequency rhythms (0.5-24 Hz) and high frequency oscillations (HFOs) (80-560 Hz). We used statistical measures to test the significant difference in PAC between the SOZ and non-seizure onset zone (NSOZ). To overcome the drawback of handcraft feature engineering, we established novel machine learning models to learn automatically the characteristics of the obtained PAC features and classify them to identify the SOZ. Besides, to handle imbalanced dataset classification, we introduced novel feature-wise/class-wise re-weighting strategies in conjunction with classifiers. In addition, we proposed a time-series nest cross-validation to provide more accurate and unbiased evaluations for this model. Seven patients with focal cortical dysplasia were included in this study. The experiment results not only showed that a significant coupling at band pairs of slow waves and HFOs exists in the SOZ when compared with the NSOZ, but also indicated the effectiveness of the PAC features and the proposed models in achieving better classification performance .
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Affiliation(s)
- Yao Miao
- Tokyo University of Agriculture and Technology, Tokyo, Japan
| | - Yasushi Iimura
- Department of Neurosurgery, Juntendo University School of Medicine, Tokyo, Japan
| | - Hidenori Sugano
- Department of Neurosurgery, Juntendo University School of Medicine, Tokyo, Japan
| | - Kosuke Fukumori
- Tokyo University of Agriculture and Technology, Tokyo, Japan
| | - Toshihisa Tanaka
- Tokyo University of Agriculture and Technology, Tokyo, Japan
- Department of Neurosurgery, Juntendo University School of Medicine, Tokyo, Japan
- RIKEN Center for Brain Science, Saitama, Japan
- RIKEN Center for Advanced Intelligent Project, Tokyo, Japan
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李 凯, 卢 俊, 余 仁, 张 锐, 陈 明. [Alterations of β-γ coupling of scalp electroencephalography during epilepsy]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2023; 40:700-708. [PMID: 37666760 PMCID: PMC10477402 DOI: 10.7507/1001-5515.202212024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 05/10/2023] [Indexed: 09/06/2023]
Abstract
Uncovering the alterations of neural interactions within the brain during epilepsy is important for the clinical diagnosis and treatment. Previous studies have shown that the phase-amplitude coupling (PAC) can be used as a potential biomarker for locating epileptic zones and characterizing the transition of epileptic phases. However, in contrast to the θ-γ coupling widely investigated in epilepsy, few studies have paid attention to the β-γ coupling, as well as its potential applications. In the current study, we use the modulation index (MI) to calculate the scalp electroencephalography (EEG)-based β-γ coupling and investigate the corresponding changes during different epileptic phases. The results show that the β-γ coupling of each brain region changes with the evolution of epilepsy, and in several brain regions, the β-γ coupling decreases during the ictal period but increases in the post-ictal period, where the differences are statistically significant. Moreover, the alterations of β-γ coupling between different brain regions can also be observed, and the strength of β-γ coupling increases in the post-ictal period, where the differences are also significant. Taken together, these findings not only contribute to understanding neural interactions within the brain during the evolution of epilepsy, but also provide a new insight into the clinical treatment.
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Affiliation(s)
- 凯杰 李
- 郑州大学 电气与信息工程学院 河南省脑科学与脑机接口技术重点实验室(郑州 450001)Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China
| | - 俊峰 卢
- 郑州大学 电气与信息工程学院 河南省脑科学与脑机接口技术重点实验室(郑州 450001)Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China
| | - 仁萍 余
- 郑州大学 电气与信息工程学院 河南省脑科学与脑机接口技术重点实验室(郑州 450001)Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China
- 郑州大学 电气与信息工程学院 中医药智能科学与工程技术研究中心(郑州 450001)Research Center for Intelligent Science and Engineering Technology of Traditional Chinese Medicine, School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China
| | - 锐 张
- 郑州大学 电气与信息工程学院 河南省脑科学与脑机接口技术重点实验室(郑州 450001)Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China
- 郑州大学 电气与信息工程学院 中医药智能科学与工程技术研究中心(郑州 450001)Research Center for Intelligent Science and Engineering Technology of Traditional Chinese Medicine, School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China
| | - 明明 陈
- 郑州大学 电气与信息工程学院 河南省脑科学与脑机接口技术重点实验室(郑州 450001)Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China
- 郑州大学 电气与信息工程学院 中医药智能科学与工程技术研究中心(郑州 450001)Research Center for Intelligent Science and Engineering Technology of Traditional Chinese Medicine, School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China
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Gallagher RS, Sinha N, Pattnaik AR, Ojemann WK, Lucas A, LaRocque JJ, Bernabei JM, Greenblatt AS, Sweeney EM, Chen HI, Davis KA, Conrad EC, Litt B. Quantifying interictal intracranial EEG to predict focal epilepsy. ARXIV 2023:arXiv:2307.15170v1. [PMID: 37547655 PMCID: PMC10402195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Introduction Intracranial EEG (IEEG) is used for 2 main purposes, to determine: (1) if epileptic networks are amenable to focal treatment and (2) where to intervene. Currently these questions are answered qualitatively and sometimes differently across centers. There is a need for objective, standardized methods to guide surgical decision making and to enable large scale data analysis across centers and prospective clinical trials. Methods We analyzed interictal data from 101 patients with drug resistant epilepsy who underwent presurgical evaluation with IEEG. We chose interictal data because of its potential to reduce the morbidity and cost associated with ictal recording. 65 patients had unifocal seizure onset on IEEG, and 36 were non-focal or multi-focal. We quantified the spatial dispersion of implanted electrodes and interictal IEEG abnormalities for each patient. We compared these measures against the "5 Sense Score (5SS)," a pre-implant estimate of the likelihood of focal seizure onset, and assessed their ability to predict the clinicians' choice of therapeutic intervention and the patient outcome. Results The spatial dispersion of IEEG electrodes predicted network focality with precision similar to the 5SS (AUC = 0.67), indicating that electrode placement accurately reflected pre-implant information. A cross-validated model combining the 5SS and the spatial dispersion of interictal IEEG abnormalities significantly improved this prediction (AUC = 0.79; p<0.05). The combined model predicted ultimate treatment strategy (surgery vs. device) with an AUC of 0.81 and post-surgical outcome at 2 years with an AUC of 0.70. The 5SS, interictal IEEG, and electrode placement were not correlated and provided complementary information. Conclusions Quantitative, interictal IEEG significantly improved upon pre-implant estimates of network focality and predicted treatment with precision approaching that of clinical experts. We present this study as an important step in building standardized, quantitative tools to guide epilepsy surgery.
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Affiliation(s)
- Ryan S Gallagher
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Perelman School of Medicine, University of Pennsylvania
| | - Nishant Sinha
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - Akash R. Pattnaik
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
| | - William K.S. Ojemann
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
| | - Alfredo Lucas
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Perelman School of Medicine, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
| | - Joshua J. LaRocque
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - John M Bernabei
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Perelman School of Medicine, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
| | | | - Elizabeth M Sweeney
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
| | - H Isaac Chen
- Department of Neurosurgery, University of Pennsylvania
- Corporal Michael J. Crescenz Veterans Affairs Medical Center
| | - Kathryn A Davis
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - Erin C Conrad
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - Brian Litt
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
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Yeh CH, Zhang C, Shi W, Lo MT, Tinkhauser G, Oswal A. Cross-Frequency Coupling and Intelligent Neuromodulation. CYBORG AND BIONIC SYSTEMS 2023; 4:0034. [PMID: 37266026 PMCID: PMC10231647 DOI: 10.34133/cbsystems.0034] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 05/02/2023] [Indexed: 06/03/2023] Open
Abstract
Cross-frequency coupling (CFC) reflects (nonlinear) interactions between signals of different frequencies. Evidence from both patient and healthy participant studies suggests that CFC plays an essential role in neuronal computation, interregional interaction, and disease pathophysiology. The present review discusses methodological advances and challenges in the computation of CFC with particular emphasis on potential solutions to spurious coupling, inferring intrinsic rhythms in a targeted frequency band, and causal interferences. We specifically focus on the literature exploring CFC in the context of cognition/memory tasks, sleep, and neurological disorders, such as Alzheimer's disease, epilepsy, and Parkinson's disease. Furthermore, we highlight the implication of CFC in the context and for the optimization of invasive and noninvasive neuromodulation and rehabilitation. Mainly, CFC could support advancing the understanding of the neurophysiology of cognition and motor control, serve as a biomarker for disease symptoms, and leverage the optimization of therapeutic interventions, e.g., closed-loop brain stimulation. Despite the evident advantages of CFC as an investigative and translational tool in neuroscience, further methodological improvements are required to facilitate practical and correct use in cyborg and bionic systems in the field.
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Affiliation(s)
- Chien-Hung Yeh
- School of Information and Electronics,
Beijing Institute of Technology, Beijing, China
| | - Chuting Zhang
- School of Information and Electronics,
Beijing Institute of Technology, Beijing, China
| | - Wenbin Shi
- School of Information and Electronics,
Beijing Institute of Technology, Beijing, China
| | - Men-Tzung Lo
- Department of Biomedical Sciences and Engineering,
National Central University, Taoyuan, Taiwan
| | - Gerd Tinkhauser
- Department of Neurology,
Bern University Hospital and University of Bern, Bern, Switzerland
| | - Ashwini Oswal
- MRC Brain Network Dynamics Unit,
University of Oxford, Oxford, UK
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Jiang X, Liu X, Liu Y, Wang Q, Li B, Zhang L. Epileptic seizures detection and the analysis of optimal seizure prediction horizon based on frequency and phase analysis. Front Neurosci 2023; 17:1191683. [PMID: 37260846 PMCID: PMC10228742 DOI: 10.3389/fnins.2023.1191683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 04/14/2023] [Indexed: 06/02/2023] Open
Abstract
Changes in the frequency composition of the human electroencephalogram are associated with the transitions to epileptic seizures. Cross-frequency coupling (CFC) is a measure of neural oscillations in different frequency bands and brain areas, and specifically phase-amplitude coupling (PAC), a form of CFC, can be used to characterize these dynamic transitions. In this study, we propose a method for seizure detection and prediction based on frequency domain analysis and PAC combined with machine learning. We analyzed two databases, the Siena Scalp EEG database and the CHB-MIT database, and used the frequency features and modulation index (MI) for time-dependent quantification. The extracted features were fed to a random forest classifier for classification and prediction. The seizure prediction horizon (SPH) was also analyzed based on the highest-performing band to maximize the time for intervention and treatment while ensuring the accuracy of the prediction. Under comprehensive consideration, the results demonstrate that better performance could be achieved at an interval length of 5 min with an average accuracy of 85.71% and 95.87% for the Siena Scalp EEG database and the CHB-MIT database, respectively. As for the adult database, the combination of PAC analysis and classification can be of significant help for seizure detection and prediction. It suggests that the rarely used SPH also has a major impact on seizure detection and prediction and further explorations for the application of PAC are needed.
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Affiliation(s)
- Ximiao Jiang
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Xiaotong Liu
- Department of Dynamics and Control, Beihang University, Beijing, China
| | - Youjun Liu
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Qingyun Wang
- Department of Dynamics and Control, Beihang University, Beijing, China
| | - Bao Li
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Liyuan Zhang
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
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Ueda T, Iimura Y, Mitsuhashi T, Suzuki H, Miao Y, Nishioka K, Tamrakar S, Matsui R, Tanaka T, Otsubo H, Sugano H, Kondo A. Chronological changes in phase-amplitude coupling during epileptic seizures in temporal lobe epilepsy. Clin Neurophysiol 2023; 148:44-51. [PMID: 36796285 DOI: 10.1016/j.clinph.2023.01.014] [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: 09/07/2022] [Revised: 12/25/2022] [Accepted: 01/19/2023] [Indexed: 02/08/2023]
Abstract
OBJECTIVE To analyze chronological changes in phase-amplitude coupling (PAC) and verify whether PAC analysis can diagnose epileptogenic zones during seizures. METHODS We analyzed 30 seizures in 10 patients with mesial temporal lobe epilepsy who had ictal discharges with preictal spiking followed by low-voltage fast activity patterns on intracranial electroencephalography. We used the amplitude of two high-frequency bands (ripples: 80-200 Hz, fast ripples: 200-300 Hz) and the phase of three slow wave bands (0.5-1 Hz, 3-4 Hz, and 4-8 Hz) for modulation index (MI) calculation from 2 minutes before seizure onset to seizure termination. We evaluated the accuracy of epileptogenic zone detection by MI, in which a combination of MI was better for diagnosis and analyzed patterns of chronological changes in MI during seizures. RESULTS MIRipples/3-4 Hz and MIRipples/4-8 Hz in the hippocampus were significantly higher than those in the peripheral regions from seizure onset. Corresponding to the phase on intracranial electroencephalography, MIRipples/3-4 Hz decreased once and subsequently increased again. MIRipples/4-8 Hz showed continuously high values. CONCLUSIONS Continuous measurement of MIRipples/3-4 Hz and MIRipples/4-8 Hz could help identify epileptogenic zones. SIGNIFICANCE PAC analysis of ictal epileptic discharges can help epileptogenic zone identification.
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Affiliation(s)
- Tetsuya Ueda
- Department of Neurosurgery, Epilepsy Center, Juntendo University, Tokyo, Japan.
| | - Yasushi Iimura
- Department of Neurosurgery, Epilepsy Center, Juntendo University, Tokyo, Japan.
| | - Takumi Mitsuhashi
- Department of Neurosurgery, Epilepsy Center, Juntendo University, Tokyo, Japan.
| | - Hiroharu Suzuki
- Department of Neurosurgery, Epilepsy Center, Juntendo University, Tokyo, Japan.
| | - Yao Miao
- Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan.
| | - Kazuki Nishioka
- Department of Neurosurgery, Epilepsy Center, Juntendo University, Tokyo, Japan.
| | - Samantha Tamrakar
- Department of Neurosurgery, Epilepsy Center, Juntendo University, Tokyo, Japan.
| | - Ryousuke Matsui
- Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan.
| | - Toshihisa Tanaka
- Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan.
| | - Hiroshi Otsubo
- Department of Neurosurgery, Epilepsy Center, Juntendo University, Tokyo, Japan; Division of Neurology, The Hospital for Sick Children, Toronto, ON, Canada.
| | - Hidenori Sugano
- Department of Neurosurgery, Epilepsy Center, Juntendo University, Tokyo, Japan.
| | - Akihide Kondo
- Department of Neurosurgery, Epilepsy Center, Juntendo University, Tokyo, Japan.
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Li C, Liu S, Wang Z, Yuan G. Classifying epileptic phase-amplitude coupling in SEEG using complex-valued convolutional neural network. Front Physiol 2023; 13:1085530. [PMID: 36685186 PMCID: PMC9849379 DOI: 10.3389/fphys.2022.1085530] [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: 10/31/2022] [Accepted: 12/20/2022] [Indexed: 01/06/2023] Open
Abstract
EEG phase-amplitude coupling (PAC), the amplitude of high-frequency oscillations modulated by the phase of low-frequency oscillations (LFOs), is a useful biomarker to localize epileptogenic tissue. It is commonly represented in a comodulogram of coupling strength but without coupled phase information. The phase-amplitude coupling is also found in the normal brain, and it is difficult to discriminate pathological phase-amplitude couplings from normal ones. This study proposes a novel approach based on complex-valued phase-amplitude coupling (CV-PAC) for classifying epileptic phase-amplitude coupling. The CV-PAC combines both the coupling strengths and the coupled phases of low-frequency oscillations. The complex-valued convolutional neural network (CV-CNN) is then used to classify epileptic CV-PAC. Stereo-electroencephalography (SEEG) recordings from nine intractable epilepsy patients were analyzed. The leave-one-out cross-validation is performed, and the area-under-curve (AUC) value is used as the indicator of the performance of different measures. Our result shows that the area-under-curve value is .92 for classifying epileptic CV-PAC using CV-CNN. The area-under-curve value decreases to .89, .80, and .88 while using traditional convolutional neural networks, support vector machine, and random forest, respectively. The phases of delta (1-4 Hz) and alpha (8-10 Hz) bands are different between epileptic and normal CV-PAC. The phase information of CV-PAC is important for improving classification performance. The proposed approach of CV-PAC/CV-CNN promises to identify more accurate epileptic brain activities for potential surgical intervention.
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Affiliation(s)
- Chunsheng Li
- Department of Biomedical Engineering, School of Electrical Engineering, Shenyang University of Technology, Shenyang, China,*Correspondence: Chunsheng Li,
| | - Shiyue Liu
- Department of Biomedical Engineering, School of Electrical Engineering, Shenyang University of Technology, Shenyang, China
| | - Zeyu Wang
- Department of Biomedical Engineering, School of Electrical Engineering, Shenyang University of Technology, Shenyang, China,Department of Electrical Engineering and Information Systems, University of Pannonia, Veszprem, Hungary
| | - Guanqian Yuan
- Department of Neurosurgery, General Hospital of Northern Theater Command, Shenyang, China
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Cherian R, Kanaga EG. Theoretical and Methodological Analysis of EEG based Seizure Detection and Prediction: An Exhaustive Review. J Neurosci Methods 2022; 369:109483. [DOI: 10.1016/j.jneumeth.2022.109483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 01/13/2022] [Accepted: 01/13/2022] [Indexed: 02/07/2023]
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