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Pitetzis D, Frantzidis C, Psoma E, Ketseridou SN, Deretzi G, Kalogera-Fountzila A, Bamidis PD, Spilioti M. The Pre-Interictal Network State in Idiopathic Generalized Epilepsies. Brain Sci 2023; 13:1671. [PMID: 38137119 PMCID: PMC10741409 DOI: 10.3390/brainsci13121671] [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: 10/29/2023] [Revised: 11/24/2023] [Accepted: 11/30/2023] [Indexed: 12/24/2023] Open
Abstract
Generalized spike wave discharges (GSWDs) are the typical electroencephalographic findings of Idiopathic Generalized Epilepsies (IGEs). These discharges are either interictal or ictal and recent evidence suggests differences in their pathogenesis. The aim of this study is to investigate, through functional connectivity analysis, the pre-interictal network state in IGEs, which precedes the formation of the interictal GSWDs. A high-density electroencephalogram (HD-EEG) was recorded in twenty-one patients with IGEs, and cortical connectivity was analyzed based on lagged coherence and individual anatomy. Graph theory analysis was used to estimate network features, assessed using the characteristic path length and clustering coefficient. The functional connectivity analysis identified two distinct networks during the pre-interictal state. These networks exhibited reversed connectivity attributes, reflecting synchronized activity at 3-4 Hz (delta2), and desynchronized activity at 8-10.5 Hz (alpha1). The delta2 network exhibited a statistically significant (p < 0.001) decrease in characteristic path length and an increase in the mean clustering coefficient. In contrast, the alpha1 network showed opposite trends in these features. The nodes influencing this state were primarily localized in the default mode network (DMN), dorsal attention network (DAN), visual network (VIS), and thalami. In conclusion, the coupling of two networks defined the pre-interictal state in IGEs. This state might be considered as a favorable condition for the generation of interictal GSWDs.
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Affiliation(s)
- Dimitrios Pitetzis
- Department of Neurology, Papageorgiou General Hospital, 56403 Thessaloniki, Greece;
- Lab of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (C.F.); (S.N.K.); (P.D.B.)
| | - Christos Frantzidis
- Lab of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (C.F.); (S.N.K.); (P.D.B.)
- School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK
| | - Elizabeth Psoma
- Department of Radiology, AHEPA General Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece; (E.P.); (A.K.-F.)
| | - Smaranda Nafsika Ketseridou
- Lab of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (C.F.); (S.N.K.); (P.D.B.)
| | - Georgia Deretzi
- Department of Neurology, Papageorgiou General Hospital, 56403 Thessaloniki, Greece;
| | - Anna Kalogera-Fountzila
- Department of Radiology, AHEPA General Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece; (E.P.); (A.K.-F.)
| | - Panagiotis D. Bamidis
- Lab of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (C.F.); (S.N.K.); (P.D.B.)
| | - Martha Spilioti
- 1st Department of Neurology, AHEPA General Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece;
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Firestone E, Sonoda M, Kuroda N, Sakakura K, Jeong JW, Lee MH, Wada K, Takayama Y, Iijima K, Iwasaki M, Miyazaki T, Asano E. Sevoflurane-induced high-frequency oscillations, effective connectivity and intraoperative classification of epileptic brain areas. Clin Neurophysiol 2023; 150:17-30. [PMID: 36989866 PMCID: PMC10192072 DOI: 10.1016/j.clinph.2023.03.004] [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: 10/24/2022] [Revised: 02/27/2023] [Accepted: 03/02/2023] [Indexed: 03/18/2023]
Abstract
OBJECTIVE To determine how sevoflurane anesthesia modulates intraoperative epilepsy biomarkers on electrocorticography, including high-frequency oscillation (HFO) effective connectivity (EC), and to investigate their relation to epileptogenicity and anatomical white matter. METHODS We studied eight pediatric drug-resistant focal epilepsy patients who achieved seizure control after invasive monitoring and resective surgery. We visualized spatial distributions of the electrocorticography biomarkers at an oxygen baseline, three time-points while sevoflurane was increasing, and at a plateau of 2 minimum alveolar concentration (MAC) sevoflurane. HFO EC was combined with diffusion-weighted imaging, in dynamic tractography. RESULTS Intraoperative HFO EC diffusely increased as a function of sevoflurane concentration, although most in epileptogenic sites (defined as those included in the resection); their ability to classify epileptogenicity was optimized at sevoflurane 2 MAC. HFO EC could be visualized on major white matter tracts, as a function of sevoflurane level. CONCLUSIONS The results strengthened the hypothesis that sevoflurane-activated HFO biomarkers may help intraoperatively localize the epileptogenic zone. SIGNIFICANCE Our results help characterize how HFOs at non-epileptogenic and epileptogenic networks respond to sevoflurane. It may be warranted to establish a normative HFO atlas incorporating the modifying effects of sevoflurane and major white matter pathways, as critical reference in epilepsy presurgical evaluation.
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Affiliation(s)
- Ethan Firestone
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center,Wayne State University, Detroit, MI 48201, USA; Department of Physiology, Wayne State University, Detroit, MI 48201, USA
| | - Masaki Sonoda
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center,Wayne State University, Detroit, MI 48201, USA; Department of Neurosurgery, Yokohama City University Graduate School of Medicine, Yokohama 2360004, Japan
| | - Naoto Kuroda
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center,Wayne State University, Detroit, MI 48201, USA; Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai 9808575, Japan
| | - Kazuki Sakakura
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center,Wayne State University, Detroit, MI 48201, USA; Department of Neurosurgery, University of Tsukuba, Tsukuba 3058575, Japan
| | - Jeong-Won Jeong
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center,Wayne State University, Detroit, MI 48201, USA; Department of Neurology, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Min-Hee Lee
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center,Wayne State University, Detroit, MI 48201, USA
| | - Keiko Wada
- Department of Anesthesiology, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Tokyo 1878551, Japan; Department of Anesthesiology and Critical Care, Yokohama City University Graduate School of Medicine, Yokohama 2360004, Japan
| | - Yutaro Takayama
- Department of Neurosurgery, Yokohama City University Graduate School of Medicine, Yokohama 2360004, Japan; Department of Neurosurgery, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Tokyo 1878551, Japan
| | - Keiya Iijima
- Department of Neurosurgery, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Tokyo 1878551, Japan
| | - Masaki Iwasaki
- Department of Neurosurgery, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Tokyo 1878551, Japan
| | - Tomoyuki Miyazaki
- Department of Anesthesiology, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Tokyo 1878551, Japan; Department of Physiology, Yokohama City University Graduate School of Medicine, Yokohama 2360004, Japan
| | - Eishi Asano
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center,Wayne State University, Detroit, MI 48201, USA; Department of Neurology, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA.
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Chiarion G, Sparacino L, Antonacci Y, Faes L, Mesin L. Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends. Bioengineering (Basel) 2023; 10:bioengineering10030372. [PMID: 36978763 PMCID: PMC10044923 DOI: 10.3390/bioengineering10030372] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/10/2023] [Accepted: 03/14/2023] [Indexed: 03/30/2023] Open
Abstract
Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks.
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Affiliation(s)
- Giovanni Chiarion
- Mathematical Biology and Physiology, Department Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy
| | - Laura Sparacino
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Yuri Antonacci
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Luca Faes
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Luca Mesin
- Mathematical Biology and Physiology, Department Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy
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韩 凌, 宋 鑫, 李 春. [Dynamic analysis of epileptic causal brain networks based on directional transfer function]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2022; 39:1082-1088. [PMID: 36575076 PMCID: PMC9927175 DOI: 10.7507/1001-5515.202202022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 10/16/2022] [Indexed: 12/29/2022]
Abstract
Epilepsy is a neurological disease with disordered brain network connectivity. It is important to analyze the brain network mechanism of epileptic seizure from the perspective of directed functional connectivity. In this paper, causal brain networks were constructed for different sub-bands of epileptic electroencephalogram (EEG) signals in interictal, preictal and ictal phases by directional transfer function method, and the information transmission pathway and dynamic change process of brain network under different conditions were analyzed. Finally, the dynamic changes of characteristic attributes of brain networks with different rhythms were analyzed. The results show that the topology of brain network changes from stochastic network to rule network during the three stage and the node connections of the whole brain network show a trend of gradual decline. The number of pathway connections between internal nodes of frontal, temporal and occipital regions increase. There are a lot of hub nodes with information outflow in the lesion region. The global efficiency in ictal stage of α, β and γ waves are significantly higher than in the interictal and the preictal stage. The clustering coefficients in preictal stage are higher than in the ictal stage and the clustering coefficients in ictal stage are higher than in the interictal stage. The clustering coefficients of frontal, temporal and parietal lobes are significantly increased. The results of this study indicate that the topological structure and characteristic properties of epileptic causal brain network can reflect the dynamic process of epileptic seizures. In the future, this study has important research value in the localization of epileptic focus and prediction of epileptic seizure.
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Affiliation(s)
- 凌 韩
- 沈阳工业大学 电气工程学院 生物医学工程系(沈阳 110870)Department of Biomedical Engineering, School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, P. R. China
| | - 鑫轲 宋
- 沈阳工业大学 电气工程学院 生物医学工程系(沈阳 110870)Department of Biomedical Engineering, School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, P. R. China
| | - 春胜 李
- 沈阳工业大学 电气工程学院 生物医学工程系(沈阳 110870)Department of Biomedical Engineering, School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, P. R. China
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Pernice R, Faes L, Feucht M, Benninger F, Mangione S, Schiecke K. Pairwise and higher-order measures of brain-heart interactions in children with temporal lobe epilepsy. J Neural Eng 2022; 19. [PMID: 35803218 DOI: 10.1088/1741-2552/ac7fba] [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: 03/28/2022] [Accepted: 07/08/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE While it is well-known that epilepsy has a clear impact on the activity of both the central nervous system (CNS) and the autonomic nervous system (ANS), its role on the complex interplay between CNS and ANS has not been fully elucidated yet. In this work, pairwise and higher-order predictability measures based on the concepts of Granger causality (GC) and Partial Information Decomposition (PID) were applied on time series of electroencephalographic (EEG) brain wave amplitude and heart rate variability (HRV) in order to investigate directed brain-heart interactions associated with the occurrence of focal epilepsy. APPROACH HRV and the envelopes of δ and α EEG activity recorded from ipsilateral (ipsi-EEG) and contralateral (contra-EEG) scalp regions were analyzed in 18 children suffering from temporal lobe epilepsy monitored during pre-ictal, ictal and post-ictal periods. After linear parametric model identification, we compared pairwise GC measures computed between HRV and a single EEG component with PID measures quantifying the unique, redundant and synergistic information transferred from ipsi-EEG and contra-EEG to HRV. MAIN RESULTS The analysis of GC revealed a dominance of the information transfer from EEG to HRV and negligible transfer from HRV to EEG, suggesting that CNS activities drive the ANS modulation of the heart rhythm, but did not evidence clear differences between δ and α rhythms, ipsi-EEG and contra-EEG, or pre- and post-ictal periods. On the contrary, PID revealed that epileptic seizures induce a reorganization of the interactions from brain to heart, as the unique predictability of HRV originated from the ipsi-EEG for the δ waves and from the contra-EEG for the α waves in the pre-ictal phase, while these patterns were reversed after the seizure. SIGNIFICANCE These results highlight the importance of considering higher-order interactions elicited by PID for the study of the neuro-autonomic effects of focal epilepsy, and may have neurophysiological and clinical implications.
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Affiliation(s)
- Riccardo Pernice
- Department of Engineering, University of Palermo, Viale delle Scienze, Bldg. 9, Palermo, 90128, ITALY
| | - Luca Faes
- Department of Engineering, University of Palermo, Viale delle Scienze, Bldg. 9, Palermo, 90128, ITALY
| | - Martha Feucht
- Epilepsy Monitoring Unit, Department of Child and Adolenscent Neuropsychiatry, University Hospital Vienna, Währinger Gürtel 18-20, Vienna, 1090, AUSTRIA
| | - Franz Benninger
- Department of Child and Adolescent Medicine, University Hospital Vienna, Währinger Gürtel 18-20, Vienna, 1090, AUSTRIA
| | - Stefano Mangione
- Department of Engineering, University of Palermo, Viale delle Scienze, Bldg. 9, Palermo, Sicilia, 90128, ITALY
| | - Karin Schiecke
- Institute of Medical Statistics, Computer Sciences and Documentation, Jena University Hospital, Friedrich Schiller University Jena, Bachstraße 18, Jena, 07743, GERMANY
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Manomaisaowapak P, Nartkulpat A, Songsiri J. Granger Causality Inference in EEG Source Connectivity Analysis: A State-Space Approach. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:3146-3156. [PMID: 34310324 DOI: 10.1109/tnnls.2021.3096642] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
This article addresses the problem of estimating brain effective connectivity from electroencephalogram (EEG) signals using a Granger causality (GC) characterized on state-space models, extended from the conventional vector autoregressive (VAR) process. The scheme involves two main steps: model estimation and model inference to estimate brain connectivity. The model estimation performs a subspace identification and active source selection based on group-norm regularized least-squares. The model inference relies on the concept of state-space GC that requires solving a Riccati equation for the covariance of estimation error. We verify the performance on simulated datasets that represent realistic human brain activities under several conditions, including percentages and location of active sources, and the number of EEG electrodes. Our model's accuracy in estimating connectivity is compared with a two-stage approach using source reconstructions and a VAR-based Granger analysis. Our method achieved better performances than the two-stage approach under the assumptions that the true source dynamics are sparse and generated from state-space models. When the method was applied to a real EEG SSVEP dataset, the temporal lobe was found to be a mediating connection between the temporal and occipital areas, which agreed with findings in previous studies.
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Nuzzi D, Stramaglia S, Javorka M, Marinazzo D, Porta A, Faes L. Extending the spectral decomposition of Granger causality to include instantaneous influences: application to the control mechanisms of heart rate variability. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200263. [PMID: 34689615 DOI: 10.1098/rsta.2020.0263] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/15/2021] [Indexed: 06/13/2023]
Abstract
Assessing Granger causality (GC) intended as the influence, in terms of reduction of variance of surprise, that a driver variable exerts on a given target, requires a suitable treatment of 'instantaneous' effects, i.e. influences due to interactions whose time scale is much faster than the time resolution of the measurements, due to unobserved confounders or insufficient sampling rate that cannot be increased because the mechanism of generation of the variable is inherently slow (e.g. the heartbeat). We exploit a recently proposed framework for the estimation of causal influences in the spectral domain and include instantaneous interactions in the modelling, thus obtaining (i) a novel index of undirected instantaneous causality and (ii) a novel measure of GC including instantaneous effects. An effective procedure to speed up the optimization of parameters in this frame is also presented. After illustrating the proposed formalism in a theoretical example, we apply it to two datasets of cardiovascular and respiratory time series and compare the values obtained within the frequency bands of physiological interest by the proposed total measure of causality with those derived from the standard GC analysis. We find that the inclusion of instantaneous causality allows us to correctly disentangle the baroreflex mechanism from the effects related to cardiorespiratory interactions. Moreover, studying how controlling the respiratory rhythm acts on cardiovascular interactions, we document an increase of the direct (non-baroreflex mediated) influence of respiration on the heart rate in the respiratory frequency band when switching from spontaneous to paced breathing. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.
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Affiliation(s)
- D Nuzzi
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari and INFN, Sezione di Bari, 70126 Bari, Italy
| | - S Stramaglia
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari and INFN, Sezione di Bari, 70126 Bari, Italy
| | - M Javorka
- Department of Physiology, Comenius University in Bratislava, Jessenius Faculty of Medicine, 03601 Martin, Slovakia
| | - D Marinazzo
- Department of Data Analysis, Ghent University, 9000 Ghent, Belgium
| | - A Porta
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
- Department of Cardiothoracic, Vascular Anesthesia and Intensive Care, IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
| | - Luca Faes
- Dipartimento di Ingegneria, Universitá di Palermo, 90128 Palermo, Italy
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Malekzadeh A, Zare A, Yaghoobi M, Kobravi HR, Alizadehsani R. Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features. SENSORS (BASEL, SWITZERLAND) 2021; 21:7710. [PMID: 34833780 PMCID: PMC8624422 DOI: 10.3390/s21227710] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/15/2021] [Accepted: 11/15/2021] [Indexed: 12/12/2022]
Abstract
Epilepsy is a brain disorder disease that affects people's quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals. The proposed method consists of three steps, including preprocessing, feature extraction, and classification. In order to perform the simulations, the Bonn and Freiburg datasets are used. Firstly, we used a band-pass filter with 0.5-40 Hz cut-off frequency for removal artifacts of the EEG datasets. Tunable-Q Wavelet Transform (TQWT) is used for EEG signal decomposition. In the second step, various linear and nonlinear features are extracted from TQWT sub-bands. In this step, various statistical, frequency, and nonlinear features are extracted from the sub-bands. The nonlinear features used are based on fractal dimensions (FDs) and entropy theories. In the classification step, different approaches based on conventional machine learning (ML) and deep learning (DL) are discussed. In this step, a CNN-RNN-based DL method with the number of layers proposed is applied. The extracted features have been fed to the input of the proposed CNN-RNN model, and satisfactory results have been reported. In the classification step, the K-fold cross-validation with k = 10 is employed to demonstrate the effectiveness of the proposed CNN-RNN classification procedure. The results revealed that the proposed CNN-RNN method for Bonn and Freiburg datasets achieved an accuracy of 99.71% and 99.13%, respectively.
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Affiliation(s)
- Anis Malekzadeh
- Department of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad 6518115743, Iran;
| | - Assef Zare
- Department of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad 6518115743, Iran;
| | - Mahdi Yaghoobi
- Department of Electrical Engineering, Mashhad Branch, Islamic Azad University, Mashhad 9187147578, Iran; (M.Y.); (H.-R.K.)
| | - Hamid-Reza Kobravi
- Department of Electrical Engineering, Mashhad Branch, Islamic Azad University, Mashhad 9187147578, Iran; (M.Y.); (H.-R.K.)
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia;
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Dimitriadis SI. Latest Advances in Human Brain Dynamics. Brain Sci 2021; 11:brainsci11111476. [PMID: 34827475 PMCID: PMC8615593 DOI: 10.3390/brainsci11111476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 10/29/2021] [Indexed: 11/16/2022] Open
Abstract
It is paramount for every neuroscientist to understand the nature of emerging technologies and approaches in investigating functional brain dynamics [...].
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Affiliation(s)
- Stavros I. Dimitriadis
- Integrative Neuroimaging Lab, 55133 Thessaloniki, Greece; or
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff CF24 4HQ, UK
- Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff CF24 4HQ, UK
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff CF24 4HQ, UK
- Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff CF24 4HQ, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff CF24 4HQ, UK
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Pernice R, Antonacci Y, Zanetti M, Busacca A, Marinazzo D, Faes L, Nollo G. Multivariate Correlation Measures Reveal Structure and Strength of Brain-Body Physiological Networks at Rest and During Mental Stress. Front Neurosci 2021; 14:602584. [PMID: 33613173 PMCID: PMC7890264 DOI: 10.3389/fnins.2020.602584] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 12/16/2020] [Indexed: 12/13/2022] Open
Abstract
In this work, we extend to the multivariate case the classical correlation analysis used in the field of network physiology to probe dynamic interactions between organ systems in the human body. To this end, we define different correlation-based measures of the multivariate interaction (MI) within and between the brain and body subnetworks of the human physiological network, represented, respectively, by the time series of δ, θ, α, and β electroencephalographic (EEG) wave amplitudes, and of heart rate, respiration amplitude, and pulse arrival time (PAT) variability (η, ρ, π). MI is computed: (i) considering all variables in the two subnetworks to evaluate overall brain-body interactions; (ii) focusing on a single target variable and dissecting its global interaction with all other variables into contributions arising from the same subnetwork and from the other subnetwork; and (iii) considering two variables conditioned to all the others to infer the network topology. The framework is applied to the time series measured from the EEG, electrocardiographic (ECG), respiration, and blood volume pulse (BVP) signals recorded synchronously via wearable sensors in a group of healthy subjects monitored at rest and during mental arithmetic and sustained attention tasks. We find that the human physiological network is highly connected, with predominance of the links internal of each subnetwork (mainly η-ρ and δ-θ, θ-α, α-β), but also statistically significant interactions between the two subnetworks (mainly η-β and η-δ). MI values are often spatially heterogeneous across the scalp and are modulated by the physiological state, as indicated by the decrease of cardiorespiratory interactions during sustained attention and by the increase of brain-heart interactions and of brain-brain interactions at the frontal scalp regions during mental arithmetic. These findings illustrate the complex and multi-faceted structure of interactions manifested within and between different physiological systems and subsystems across different levels of mental stress.
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Affiliation(s)
- Riccardo Pernice
- Department of Engineering, University of Palermo, Palermo, Italy
| | - Yuri Antonacci
- Department of Physics and Chemistry “Emilio Segrè,” University of Palermo, Palermo, Italy
| | - Matteo Zanetti
- Department of Industrial Engineering, University of Trento, Trento, Italy
| | | | | | - Luca Faes
- Department of Engineering, University of Palermo, Palermo, Italy
| | - Giandomenico Nollo
- Department of Industrial Engineering, University of Trento, Trento, Italy
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