1
|
Hiwaki O. Whole-Head Noninvasive Brain Signal Measurement System with High Temporal and Spatial Resolution Using Static Magnetic Field Bias to the Brain. Bioengineering (Basel) 2024; 11:917. [PMID: 39329659 PMCID: PMC11428585 DOI: 10.3390/bioengineering11090917] [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: 08/10/2024] [Revised: 09/08/2024] [Accepted: 09/11/2024] [Indexed: 09/28/2024] Open
Abstract
Noninvasive brain signal measurement techniques are crucial for understanding human brain function and brain-machine interface applications. Conventionally, noninvasive brain signal measurement techniques, such as electroencephalography, magnetoencephalography, functional magnetic resonance imaging, and near-infrared spectroscopy, have been developed. However, currently, there is no practical noninvasive technique to measure brain function with high temporal and spatial resolution using one instrument. We developed a novel noninvasive brain signal measurement technique with high temporal and spatial resolution by biasing a static magnetic field emitted from a coil on the head to the brain. In this study, we applied this technique to develop a groundbreaking system for noninvasive whole-head brain function measurement with high spatiotemporal resolution across the entire head. We validated this system by measuring movement-related brain signals evoked by a right index finger extension movement and demonstrated that the proposed system can measure the dynamic activity of brain regions involved in finger movement with high spatiotemporal accuracy over the whole brain.
Collapse
Affiliation(s)
- Osamu Hiwaki
- Graduate School of Information Sciences, Hiroshima City University, 3-4-1 Ozuka-Higashi, Asa-Minami-Ku, Hiroshima 731-3194, Japan
| |
Collapse
|
2
|
Shen M, Yang F, Wen P, Song B, Li Y. A real-time epilepsy seizure detection approach based on EEG using short-time Fourier transform and Google-Net convolutional neural network. Heliyon 2024; 10:e31827. [PMID: 38845915 PMCID: PMC11153222 DOI: 10.1016/j.heliyon.2024.e31827] [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: 06/15/2023] [Revised: 05/19/2024] [Accepted: 05/22/2024] [Indexed: 06/09/2024] Open
Abstract
Epilepsy is one of the most common brain disorders, and seizures of epilepsy have severe adverse effects on patients. Real-time epilepsy seizure detection using electroencephalography (EEG) signals is an important research area aimed at improving the diagnosis and treatment of epilepsy. This paper proposed a real-time approach based on EEG signal for detecting epilepsy seizures using the STFT and Google-net convolutional neural network (CNN). The CHB-MIT database was used to evaluate the performance, and received the results of 97.74 % in accuracy, 98.90 % in sensitivity, 1.94 % in false positive rate. Additionally, the proposed method was implemented in a real-time manner using the sliding window technique. The processing time of the proposed method just 0.02 s for every 2-s EEG episode and achieved average 9.85- second delay in each seizure onset.
Collapse
Affiliation(s)
- Mingkan Shen
- School of Engineering, University of Southern Queensland, Toowoomba, Australia
| | - Fuwen Yang
- School of Engineering and Built Environment, Griffith University, Gold Coast, Australia
| | - Peng Wen
- School of Engineering, University of Southern Queensland, Toowoomba, Australia
| | - Bo Song
- School of Engineering, University of Southern Queensland, Toowoomba, Australia
| | - Yan Li
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Australia
| |
Collapse
|
3
|
Awais M, Belhaouari SB, Kassoul K. Graphical Insight: Revolutionizing Seizure Detection with EEG Representation. Biomedicines 2024; 12:1283. [PMID: 38927490 PMCID: PMC11201274 DOI: 10.3390/biomedicines12061283] [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: 04/30/2024] [Revised: 05/28/2024] [Accepted: 05/31/2024] [Indexed: 06/28/2024] Open
Abstract
Epilepsy is characterized by recurring seizures that result from abnormal electrical activity in the brain. These seizures manifest as various symptoms including muscle contractions and loss of consciousness. The challenging task of detecting epileptic seizures involves classifying electroencephalography (EEG) signals into ictal (seizure) and interictal (non-seizure) classes. This classification is crucial because it distinguishes between the states of seizure and seizure-free periods in patients with epilepsy. Our study presents an innovative approach for detecting seizures and neurological diseases using EEG signals by leveraging graph neural networks. This method effectively addresses EEG data processing challenges. We construct a graph representation of EEG signals by extracting features such as frequency-based, statistical-based, and Daubechies wavelet transform features. This graph representation allows for potential differentiation between seizure and non-seizure signals through visual inspection of the extracted features. To enhance seizure detection accuracy, we employ two models: one combining a graph convolutional network (GCN) with long short-term memory (LSTM) and the other combining a GCN with balanced random forest (BRF). Our experimental results reveal that both models significantly improve seizure detection accuracy, surpassing previous methods. Despite simplifying our approach by reducing channels, our research reveals a consistent performance, showing a significant advancement in neurodegenerative disease detection. Our models accurately identify seizures in EEG signals, underscoring the potential of graph neural networks. The streamlined method not only maintains effectiveness with fewer channels but also offers a visually distinguishable approach for discerning seizure classes. This research opens avenues for EEG analysis, emphasizing the impact of graph representations in advancing our understanding of neurodegenerative diseases.
Collapse
Affiliation(s)
- Muhammad Awais
- Department of Creative Technologies, Air University, Islamabad 44000, Pakistan;
| | - Samir Brahim Belhaouari
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha 5825, Qatar
| | - Khelil Kassoul
- Geneva School of Business Administration, University of Applied Sciences Western Switzerland, HES-SO, 1227 Geneva, Switzerland
| |
Collapse
|
4
|
Menceloglu M, Grabowecky M, Suzuki S. A phase-shifting anterior-posterior network organizes global phase relations. PLoS One 2024; 19:e0296827. [PMID: 38346024 PMCID: PMC10861041 DOI: 10.1371/journal.pone.0296827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 12/19/2023] [Indexed: 02/15/2024] Open
Abstract
Prior research has identified a variety of task-dependent networks that form through inter-regional phase-locking of oscillatory activity that are neural correlates of specific behaviors. Despite ample knowledge of task-specific functional networks, general rules governing global phase relations have not been investigated. To discover such general rules, we focused on phase modularity, measured as the degree to which global phase relations in EEG comprised distinct synchronized clusters interacting with one another at large phase lags. Synchronized clusters were detected with a standard community-detection algorithm, and the degree of phase modularity was quantified by the index q. Notably, we found that the mechanism controlling phase modularity is remarkably simple. A network comprising anterior-posterior long-distance connectivity coherently shifted phase relations from low-angles (|Δθ| < π/4) in low-modularity states (bottom 5% in q) to high-angles (|Δθ| > 3π/4) in high-modularity states (top 5% in q), accounting for fluctuations in phase modularity. This anterior-posterior network may play a fundamental functional role as (1) it controls phase modularity across a broad range of frequencies (3-50 Hz examined) in different behavioral conditions (resting with the eyes closed or watching a silent nature video) and (2) neural interactions (measured as power correlations) in beta-to-gamma bands were consistently elevated in high-modularity states. These results may motivate future investigations into the functional roles of phase modularity as well as the anterior-posterior network that controls it.
Collapse
Affiliation(s)
- Melisa Menceloglu
- Department of Psychology, Northwestern University, Evanston, Illinois, United States of America
| | - Marcia Grabowecky
- Department of Psychology, Northwestern University, Evanston, Illinois, United States of America
- Interdepartmental Neuroscience, Northwestern University, Evanston, Illinois, United States of America
| | - Satoru Suzuki
- Department of Psychology, Northwestern University, Evanston, Illinois, United States of America
- Interdepartmental Neuroscience, Northwestern University, Evanston, Illinois, United States of America
| |
Collapse
|
5
|
Khan SU, Jan SU, Koo I. Robust Epileptic Seizure Detection Using Long Short-Term Memory and Feature Fusion of Compressed Time-Frequency EEG Images. SENSORS (BASEL, SWITZERLAND) 2023; 23:9572. [PMID: 38067944 PMCID: PMC10708722 DOI: 10.3390/s23239572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/27/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023]
Abstract
Epilepsy is a prevalent neurological disorder with considerable risks, including physical impairment and irreversible brain damage from seizures. Given these challenges, the urgency for prompt and accurate seizure detection cannot be overstated. Traditionally, experts have relied on manual EEG signal analyses for seizure detection, which is labor-intensive and prone to human error. Recognizing this limitation, the rise in deep learning methods has been heralded as a promising avenue, offering more refined diagnostic precision. On the other hand, the prevailing challenge in many models is their constrained emphasis on specific domains, potentially diminishing their robustness and precision in complex real-world environments. This paper presents a novel model that seamlessly integrates the salient features from the time-frequency domain along with pivotal statistical attributes derived from EEG signals. This fusion process involves the integration of essential statistics, including the mean, median, and variance, combined with the rich data from compressed time-frequency (CWT) images processed using autoencoders. This multidimensional feature set provides a robust foundation for subsequent analytic steps. A long short-term memory (LSTM) network, meticulously optimized for the renowned Bonn Epilepsy dataset, was used to enhance the capability of the proposed model. Preliminary evaluations underscore the prowess of the proposed model: a remarkable 100% accuracy in most of the binary classifications, exceeding 95% accuracy in three-class and four-class challenges, and a commendable rate, exceeding 93.5% for the five-class classification.
Collapse
Affiliation(s)
- Shafi Ullah Khan
- Department of Electrical Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea
| | - Sana Ullah Jan
- School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK;
| | - Insoo Koo
- Department of Electrical Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea
| |
Collapse
|
6
|
Attar ET. Integrated Biosignal Analysis to Provide Biomarkers for Recognizing Time Perception Difficulties. JOURNAL OF MEDICAL SIGNALS & SENSORS 2023; 13:217-223. [PMID: 37622046 PMCID: PMC10445675 DOI: 10.4103/jmss.jmss_24_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 09/06/2022] [Accepted: 10/01/2022] [Indexed: 08/26/2023]
Abstract
Background Time perception refers to the capability to recognize the passage of time. The cerebellum is located at the back of the brain, underlying the occipital and temporal lobes. Dyschronometria is a cerebellar dysfunction, in which a person cannot precisely estimate the amount of time that has passed. Cardiac indicators such as heart rate (HR) variability have been associated with mental function in healthy individuals. Moreover, time perception has been previously studied concerning cardiac signs. Human time perception is influenced by various factors such as attention and drowsiness. An electroencephalogram (EEG) is a suitable modality for evaluating cortical reactions due to its affordability and usefulness. Because EEG has a high sequential outcome, it offers valuable data to explore variability in psychological situations. An electrocardiogram (ECG) records electrical signals from the heart to examine various heart conditions. The electromyography (EMG) technique detects electrical impulses produced by muscles. Methods EEG, ECG, and EMG are integrated during time perception. This study evaluated the human body's time perception through the neurological, cardiovascular, and muscular systems using a simple neurofeedback exercise after time perception tasks. The three biosignals which are EEG, ECG, and EMG were investigated to use them as biomarkers for recognizing time perception difficulty as the main goal of the study. Five healthy college students with no health issues participated, and their EEG, ECG, and EMG were recorded while relaxing and performing a time wall estimation task and neurofeedback training. Previous research has shown the relationship between EEG frequency bands and the frontal center during time perception. Investigating the connection between ECG, EEG, and EMG under time perception conditions is significant. Results The results show that ECG (HR), EEG (Delta wave), and EMG (root mean square) are critical features in time perception difficulties. Conclusion The ability and outcomes of multiple biomarkers might allow for improved diagnosis and monitoring of the progress of any treatment applications such as biofeedback training. Furthermore, those biomarkers could be used as useful for evaluating and treating dyschronometria.
Collapse
Affiliation(s)
- Eyad Talal Attar
- Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
| |
Collapse
|
7
|
Singh MS, Pasumarthy R, Vaidya U, Leonhardt S. On quantification and maximization of information transfer in network dynamical systems. Sci Rep 2023; 13:5588. [PMID: 37019948 PMCID: PMC10076297 DOI: 10.1038/s41598-023-32762-7] [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: 10/19/2022] [Accepted: 04/01/2023] [Indexed: 04/07/2023] Open
Abstract
Information flow among nodes in a complex network describes the overall cause-effect relationships among the nodes and provides a better understanding of the contributions of these nodes individually or collectively towards the underlying network dynamics. Variations in network topologies result in varying information flows among nodes. We integrate theories from information science with control network theory into a framework that enables us to quantify and control the information flows among the nodes in a complex network. The framework explicates the relationships between the network topology and the functional patterns, such as the information transfers in biological networks, information rerouting in sensor nodes, and influence patterns in social networks. We show that by designing or re-configuring the network topology, we can optimize the information transfer function between two chosen nodes. As a proof of concept, we apply our proposed methods in the context of brain networks, where we reconfigure neural circuits to optimize excitation levels among the excitatory neurons.
Collapse
Affiliation(s)
| | | | - Umesh Vaidya
- Mechanical Department, Clemson University, Clemson, USA
| | - Steffen Leonhardt
- Chair for Medical Information Technology, RWTH Aachen University, Aachen, Germany
| |
Collapse
|
8
|
Real-time epilepsy seizure detection based on EEG using tunable-Q wavelet transform and convolutional neural network. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
|
9
|
Ren Z, Han X, Wang B. The performance evaluation of the state-of-the-art EEG-based seizure prediction models. Front Neurol 2022; 13:1016224. [PMID: 36504642 PMCID: PMC9732735 DOI: 10.3389/fneur.2022.1016224] [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: 08/10/2022] [Accepted: 11/09/2022] [Indexed: 11/26/2022] Open
Abstract
The recurrent and unpredictable nature of seizures can lead to unintentional injuries and even death. The rapid development of electroencephalogram (EEG) and Artificial Intelligence (AI) technologies has made it possible to predict seizures in real-time through brain-machine interfaces (BCI), allowing advanced intervention. To date, there is still much room for improvement in predictive seizure models constructed by EEG using machine learning (ML) and deep learning (DL). But, the most critical issue is how to improve the performance and generalization of the model, which involves some confusing conceptual and methodological issues. This review focuses on analyzing several factors affecting the performance of seizure prediction models, focusing on the aspects of post-processing, seizure occurrence period (SOP), seizure prediction horizon (SPH), and algorithms. Furthermore, this study presents some new directions and suggestions for building high-performance prediction models in the future. We aimed to clarify the concept for future research in related fields and improve the performance of prediction models to provide a theoretical basis for future applications of wearable seizure detection devices.
Collapse
Affiliation(s)
- Zhe Ren
- Department of Neurology, Zhengzhou University People's Hospital, Zhengzhou, China,Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Xiong Han
- Department of Neurology, Zhengzhou University People's Hospital, Zhengzhou, China,Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, China,*Correspondence: Xiong Han
| | - Bin Wang
- Department of Neurology, Zhengzhou University People's Hospital, Zhengzhou, China,Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, China
| |
Collapse
|
10
|
An EEG based real-time epilepsy seizure detection approach using discrete wavelet transform and machine learning methods. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103820] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
11
|
Beumer S, Boon P, Klooster DCW, van Ee R, Carrette E, Paulides MM, Mestrom RMC. Personalized tDCS for Focal Epilepsy—A Narrative Review: A Data-Driven Workflow Based on Imaging and EEG Data. Brain Sci 2022; 12:brainsci12050610. [PMID: 35624997 PMCID: PMC9139054 DOI: 10.3390/brainsci12050610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/03/2022] [Accepted: 05/05/2022] [Indexed: 02/01/2023] Open
Abstract
Conventional transcranial electric stimulation(tES) using standard anatomical positions for the electrodes and standard stimulation currents is frequently not sufficiently selective in targeting and reaching specific brain locations, leading to suboptimal application of electric fields. Recent advancements in in vivo electric field characterization may enable clinical researchers to derive better relationships between the electric field strength and the clinical results. Subject-specific electric field simulations could lead to improved electrode placement and more efficient treatments. Through this narrative review, we present a processing workflow to personalize tES for focal epilepsy, for which there is a clear cortical target to stimulate. The workflow utilizes clinical imaging and electroencephalography data and enables us to relate the simulated fields to clinical outcomes. We review and analyze the relevant literature for the processing steps in the workflow, which are the following: tissue segmentation, source localization, and stimulation optimization. In addition, we identify shortcomings and ongoing trends with regard to, for example, segmentation quality and tissue conductivity measurements. The presented processing steps result in personalized tES based on metrics like focality and field strength, which allow for correlation with clinical outcomes.
Collapse
Affiliation(s)
- Steven Beumer
- Department of Electrical Engineering, University of Technology Eindhoven, P.O. Box 513, 5600 MB Eindhoven, The Netherlands; (P.B.); (D.C.W.K.); (E.C.); (M.M.P.); (R.M.C.M.)
- Correspondence:
| | - Paul Boon
- Department of Electrical Engineering, University of Technology Eindhoven, P.O. Box 513, 5600 MB Eindhoven, The Netherlands; (P.B.); (D.C.W.K.); (E.C.); (M.M.P.); (R.M.C.M.)
- Department of Neurology, Ghent University Hospital, Corneel Heymanslaan 10, 9000 Ghent, Belgium
| | - Debby C. W. Klooster
- Department of Electrical Engineering, University of Technology Eindhoven, P.O. Box 513, 5600 MB Eindhoven, The Netherlands; (P.B.); (D.C.W.K.); (E.C.); (M.M.P.); (R.M.C.M.)
- Department of Neurology, Ghent University Hospital, Corneel Heymanslaan 10, 9000 Ghent, Belgium
| | - Raymond van Ee
- Philips Research Eindhoven, High Tech Campus 34, 5656 AE Eindhoven, The Netherlands;
| | - Evelien Carrette
- Department of Electrical Engineering, University of Technology Eindhoven, P.O. Box 513, 5600 MB Eindhoven, The Netherlands; (P.B.); (D.C.W.K.); (E.C.); (M.M.P.); (R.M.C.M.)
- Department of Neurology, Ghent University Hospital, Corneel Heymanslaan 10, 9000 Ghent, Belgium
| | - Maarten M. Paulides
- Department of Electrical Engineering, University of Technology Eindhoven, P.O. Box 513, 5600 MB Eindhoven, The Netherlands; (P.B.); (D.C.W.K.); (E.C.); (M.M.P.); (R.M.C.M.)
- Department of Radiation Oncology, Erasmus Medical Center Cancer Institute, Burgemeester Oudlaan 50, 3062 PA Rotterdam, The Netherlands
| | - Rob M. C. Mestrom
- Department of Electrical Engineering, University of Technology Eindhoven, P.O. Box 513, 5600 MB Eindhoven, The Netherlands; (P.B.); (D.C.W.K.); (E.C.); (M.M.P.); (R.M.C.M.)
| |
Collapse
|
12
|
Elezi L, Koren JP, Pirker S, Baumgartner C. Automatic seizure detection and seizure pattern morphology. Clin Neurophysiol 2022; 138:214-220. [DOI: 10.1016/j.clinph.2022.02.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 02/09/2022] [Accepted: 02/18/2022] [Indexed: 11/28/2022]
|
13
|
Ambati R, Raja S, Al-Hameed M, John T, Arjoune Y, Shekhar R. Neuromorphic Architecture Accelerated Automated Seizure Detection in Multi-Channel Scalp EEG. SENSORS 2022; 22:s22051852. [PMID: 35271005 PMCID: PMC8914704 DOI: 10.3390/s22051852] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 02/22/2022] [Accepted: 02/23/2022] [Indexed: 11/16/2022]
Abstract
Epileptic focal seizures can be localized in the brain using tracer injections during or immediately after the incidence of a seizure. A real-time automated seizure detection system with minimal latency can help time the injection properly to find the seizure origin accurately. Reliable real-time seizure detection systems have not been clinically reported yet. We developed an anomaly detection-based automated seizure detection system, using scalp-electroencephalogram (EEG) data, which can be trained using a few seizure sessions, and implemented it on commercially available hardware with parallel, neuromorphic architecture—the NeuroStack. We extracted nonlinear, statistical, and discrete wavelet decomposition features, and we developed a graphical user interface and traditional feature selection methods to select the most discriminative features. We investigated Reduced Coulomb Energy (RCE) networks and K-Nearest Neighbors (k-NN) for its several advantages, such as fast learning no local minima problem. We obtained a maximum sensitivity of 91.14%±1.77% and a specificity of 98.77%±0.57% with 5 s epoch duration. The system’s latency was 12 s, which is within most seizure event windows, which last for an average duration of 60 s. Our results showed that the CD feature consumes large computation resources and excluding it can reduce the latency to 3.6 s but at the cost of lower performance 80% sensitivity and 97% specificity. We demonstrated that the proposed methodology achieves a high specificity and an acceptable sensitivity within a short delay. Our results indicated also that individual-based RCE are superior to population-based RCE. The proposed RCE networks has been compared to SVM and ANN as a baseline for comparison as they are the most common machine learning seizure detection methods. SVM and ANN-based systems were trained on the same data as RCE and K-NN with features optimized specifically for them. RCE nets are superior to SVM and ANN. The proposed model also achieves comparable performance to the state-of-the-art deep learning techniques while not requiring a sizeable database, which is often expensive to build. These numbers indicate that the system is viable as a trigger mechanism for tracer injection.
Collapse
Affiliation(s)
- Ravi Ambati
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC 20010, USA; (R.A.); (T.J.); (Y.A.)
| | - Shanker Raja
- National Neuroscience Institute, King Fahad Medical City, Riyadh 12231, Saudi Arabia; (S.R.); (M.A.-H.)
| | - Majed Al-Hameed
- National Neuroscience Institute, King Fahad Medical City, Riyadh 12231, Saudi Arabia; (S.R.); (M.A.-H.)
| | - Titus John
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC 20010, USA; (R.A.); (T.J.); (Y.A.)
| | - Youness Arjoune
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC 20010, USA; (R.A.); (T.J.); (Y.A.)
| | - Raj Shekhar
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC 20010, USA; (R.A.); (T.J.); (Y.A.)
- Correspondence:
| |
Collapse
|
14
|
A survey of brain network analysis by electroencephalographic signals. Cogn Neurodyn 2022; 16:17-41. [PMID: 35126769 PMCID: PMC8807775 DOI: 10.1007/s11571-021-09689-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 04/25/2021] [Accepted: 05/31/2021] [Indexed: 02/03/2023] Open
Abstract
Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.
Collapse
|
15
|
Dissanayake T, Fernando T, Denman S, Sridharan S, Fookes C. Geometric Deep Learning for Subject Independent Epileptic Seizure Prediction Using Scalp EEG Signals. IEEE J Biomed Health Inform 2022; 26:527-538. [PMID: 34314363 DOI: 10.1109/jsen.2021.3057076] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Recently, researchers in the biomedical community have introduced deep learning-based epileptic seizure prediction models using electroencephalograms (EEGs) that can anticipate an epileptic seizure by differentiating between the pre-ictal and interictal stages of the subject's brain. Despite having the appearance of a typical anomaly detection task, this problem is complicated by subject-specific characteristics in EEG data. Therefore, studies that investigate seizure prediction widely employ subject-specific models. However, this approach is not suitable in situations where a target subject has limited (or no) data for training. Subject-independent models can address this issue by learning to predict seizures from multiple subjects, and therefore are of greater value in practice. In this study, we propose a subject-independent seizure predictor using Geometric Deep Learning (GDL). In the first stage of our GDL-based method we use graphs derived from physical connections in the EEG grid. We subsequently seek to synthesize subject-specific graphs using deep learning. The models proposed in both stages achieve state-of-the-art performance using a one-hour early seizure prediction window on two benchmark datasets (CHB-MIT-EEG: 95.38% with 23 subjects and Siena-EEG: 96.05% with 15 subjects). To the best of our knowledge, this is the first study that proposes synthesizing subject-specific graphs for seizure prediction. Furthermore, through model interpretation we outline how this method can potentially contribute towards Scalp EEG-based seizure localization.
Collapse
|
16
|
Abdulhussien AS, AbdulSaddaa AT, Iqbal K. Automatic seizure detection with different time delays using SDFT and time-domain feature extraction. J Biomed Res 2022; 36:48-57. [PMID: 35403610 PMCID: PMC8894282 DOI: 10.7555/jbr.36.20210124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Affiliation(s)
- Amal S. Abdulhussien
- Communication Technical Engineering College, Al-Furat Al-Awsat Technical University, Al-Najaf 54001, Iraq
- Amal Salman Abdulhussien, Communication Technical Engineering College, Al-Furat Al-Awsat Technical University, abylon-najaf street, Al-Najaf 540001, Iraq. Tel: +964-771-674-2333. E-mail:
| | - Ahmad T. AbdulSaddaa
- Communication Technical Engineering College, Al-Furat Al-Awsat Technical University, Al-Najaf 54001, Iraq
| | - Kamran Iqbal
- Department of System Engineering, University of Arkansas at Little Rock, Little Rock, AR 72204, USA
| |
Collapse
|
17
|
Gu H, Chou CA. Optimizing non-uniform multivariate embedding for multiscale entropy analysis of complex systems. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
|
18
|
Gu H, Chou CA. Detecting Epileptic Seizures via Non-Uniform Multivariate Embedding of EEG Signals. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:1690-1693. [PMID: 34891611 DOI: 10.1109/embc46164.2021.9630130] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Efficient real-time detection of epileptic seizures remains a challenging task in clinical practice. In this study, we introduce a new thresholding method to monitor brain activities via a non-uniform multivariate (NUM) embedding of multi-channel electroencephalogram (EEG) signals. Specifically, we present a NUM embedding optimization problem to identify the best embedding parameters. We originate one feature, named non-uniform multivariate multiscale entropy (NUMME), which is extracted from the NUM embedded EEG data. Finally, the extracted feature, compared to an individualized threshold, is used for monitoring and detecting seizure onsets. Experimental results on the real CHB-MIT Scalp EEG database show that our approach achieves a comparable performance to the state-of-art methods. Moreover, it is important to note that we accomplish this without using any sophisticated machine learning algorithms.
Collapse
|
19
|
Giacopelli G, Tegolo D, Migliore M. The role of network connectivity on epileptiform activity. Sci Rep 2021; 11:20792. [PMID: 34675264 PMCID: PMC8531347 DOI: 10.1038/s41598-021-00283-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Accepted: 08/27/2021] [Indexed: 11/09/2022] Open
Abstract
A number of potentially important mechanisms have been identified as key players to generate epileptiform activity, such as genetic mutations, activity-dependent alteration of synaptic functions, and functional network reorganization at the macroscopic level. Here we study how network connectivity at cellular level can affect the onset of epileptiform activity, using computational model networks with different wiring properties. The model suggests that networks connected as in real brain circuits are more resistant to generate seizure-like activity. The results suggest new experimentally testable predictions on the cellular network connectivity in epileptic individuals, and highlight the importance of using the appropriate network connectivity to investigate epileptiform activity with computational models.
Collapse
Affiliation(s)
- G Giacopelli
- Department of Mathematics and Informatics, University of Palermo, Palermo, Italy.,Institute of Biophysics, National Research Council, Palermo, Italy
| | - D Tegolo
- Department of Mathematics and Informatics, University of Palermo, Palermo, Italy.,Institute of Biophysics, National Research Council, Palermo, Italy
| | - M Migliore
- Institute of Biophysics, National Research Council, Palermo, Italy.
| |
Collapse
|
20
|
An Introduction to Neonatal EEG. J Perinat Neonatal Nurs 2021; 35:369-376. [PMID: 34726654 DOI: 10.1097/jpn.0000000000000599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Newborn care has witnessed significant improvements in survival, but ongoing concerns persist about neurodevelopmental outcome. Protecting the newborn brain is the focus of neurocritical care in the intensive care unit. Brain-focused care places emphasis on clinical practices supporting neurodevelopment in conjunction with early detection, diagnosis, and treatment of brain injury. Technology now facilitates continuous cot-side monitoring of brain function. Neuromonitoring techniques in neonatal intensive care units include the use of electroencephalography (EEG) or amplitude-integrated EEG (aEEG) and near-infrared spectroscopy. This article aims to provide an introduction to EEG, which is appropriate for neonatal healthcare professionals.
Collapse
|
21
|
Li H, Zhang Q, Lin Z, Gao F. Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network. Brain Sci 2021; 11:1066. [PMID: 34439685 PMCID: PMC8392428 DOI: 10.3390/brainsci11081066] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 08/05/2021] [Accepted: 08/11/2021] [Indexed: 11/23/2022] Open
Abstract
Epilepsy is a chronic neurological disorder which can affect 65 million patients worldwide. Recently, network based analyses have been of great help in the investigation of seizures. Now graph theory is commonly applied to analyze functional brain networks, but functional brain networks are dynamic. Methods based on graph theory find it difficult to reflect the dynamic changes of functional brain network. In this paper, an approach to extracting features from brain functional networks is presented. Dynamic functional brain networks can be obtained by stacking multiple functional brain networks on the time axis. Then, a tensor decomposition method is used to extract features, and an ELM classifier is introduced to complete epilepsy prediction. In the prediction of epilepsy, the accuracy and F1 score of the feature extracted by tensor decomposition are higher than the degree and clustering coefficient. The features extracted from the dynamic functional brain network by tensor decomposition show better and more comprehensive performance than degree and clustering coefficient in epilepsy prediction.
Collapse
Affiliation(s)
| | - Qizhong Zhang
- Institute of Intelligent Control and Robotics, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; (H.L.); (Z.L.); (F.G.)
| | | | | |
Collapse
|
22
|
Qian S, Chou CA. A Koopman-operator-theoretical approach for anomaly recognition and detection of multi-variate EEG system. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102911] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
23
|
Dissanayake T, Fernando T, Denman S, Sridharan S, Fookes C. Geometric Deep Learning for Subject-Independent Epileptic Seizure Prediction using Scalp EEG Signals. IEEE J Biomed Health Inform 2021; 26:527-538. [PMID: 34314363 DOI: 10.1109/jbhi.2021.3100297] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Recently, researchers in the biomedical community have introduced deep learning-based epileptic seizure prediction models using electroencephalograms (EEGs) that can anticipate an epileptic seizure by differentiating between the pre-ictal and interictal stages of the subjects brain. Despite having the appearance of a typical anomaly detection task, this problem is complicated by subject-specific characteristics in EEG data. Therefore, studies that investigate seizure prediction widely employ subject-specific models. However, this approach is not suitable in situations where a target subject has limited (or no) data for training. Subject-independent models can address this issue by learning to predict seizures from multiple subjects, and therefore are of greater value in practice. In this study, we propose a subject-independent seizure predictor using Geometric Deep Learning (GDL). In the first stage of our GDL-based method we use graphs derived from physical connections in the EEG grid. We subsequently seek to synthesize subject-specific graphs using deep learning. The models proposed in both stages achieve state-of-the-art performance using a one-hour early seizure prediction window on two benchmark datasets (CHB-MIT-EEG: 95.38% with 23 subjects and Siena-EEG: 96.05% with 15 subjects). To the best of our knowledge, this is the first study that proposes synthesizing subject-specific graphs for seizure prediction. Furthermore, through model interpretation we outline how this method can potentially contribute towards Scalp EEG-based seizure localization.
Collapse
|
24
|
Menceloglu M, Grabowecky M, Suzuki S. Spatiotemporal dynamics of maximal and minimal EEG spectral power. PLoS One 2021; 16:e0253813. [PMID: 34283869 PMCID: PMC8291701 DOI: 10.1371/journal.pone.0253813] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 06/11/2021] [Indexed: 11/18/2022] Open
Abstract
Oscillatory neural activities are prevalent in the brain with their phase realignment contributing to the coordination of neural communication. Phase realignments may have especially strong (or weak) impact when neural activities are strongly synchronized (or desynchronized) within the interacting populations. We report that the spatiotemporal dynamics of strong regional synchronization measured as maximal EEG spectral power-referred to as activation-and strong regional desynchronization measured as minimal EEG spectral power-referred to as suppression-are characterized by the spatial segregation of small-scale and large-scale networks. Specifically, small-scale spectral-power activations and suppressions involving only 2-7% (1-4 of 60) of EEG scalp sites were prolonged (relative to stochastic dynamics) and consistently co-localized in a frequency specific manner. For example, the small-scale networks for θ, α, β1, and β2 bands (4-30 Hz) consistently included frontal sites when the eyes were closed, whereas the small-scale network for γ band (31-55 Hz) consistently clustered in medial-central-posterior sites whether the eyes were open or closed. Large-scale activations and suppressions involving over 17-30% (10-18 of 60) of EEG sites were also prolonged and generally clustered in regions complementary to where small-scale activations and suppressions clustered. In contrast, intermediate-scale activations and suppressions (involving 7-17% of EEG sites) tended to follow stochastic dynamics and were less consistently localized. These results suggest that strong synchronizations and desynchronizations tend to occur in small-scale and large-scale networks that are spatially segregated and frequency specific. These synchronization networks may broadly segregate the relatively independent and highly cooperative oscillatory processes while phase realignments fine-tune the network configurations based on behavioral demands.
Collapse
Affiliation(s)
- Melisa Menceloglu
- Department of Psychology, Northwestern University, Evanston, IL, United States of America
| | - Marcia Grabowecky
- Department of Psychology, Northwestern University, Evanston, IL, United States of America
- Interdepartmental Neuroscience, Northwestern University, Evanston, IL, United States of America
| | - Satoru Suzuki
- Department of Psychology, Northwestern University, Evanston, IL, United States of America
- Interdepartmental Neuroscience, Northwestern University, Evanston, IL, United States of America
- * E-mail:
| |
Collapse
|
25
|
Martini ML, Valliani AA, Sun C, Costa AB, Zhao S, Panov F, Ghatan S, Rajan K, Oermann EK. Deep anomaly detection of seizures with paired stereoelectroencephalography and video recordings. Sci Rep 2021; 11:7482. [PMID: 33820942 PMCID: PMC8021582 DOI: 10.1038/s41598-021-86891-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Accepted: 03/16/2021] [Indexed: 01/30/2023] Open
Abstract
Real-time seizure detection is a resource intensive process as it requires continuous monitoring of patients on stereoelectroencephalography. This study improves real-time seizure detection in drug resistant epilepsy (DRE) patients by developing patient-specific deep learning models that utilize a novel self-supervised dynamic thresholding approach. Deep neural networks were constructed on over 2000 h of high-resolution, multichannel SEEG and video recordings from 14 DRE patients. Consensus labels from a panel of epileptologists were used to evaluate model efficacy. Self-supervised dynamic thresholding exhibited improvements in positive predictive value (PPV; difference: 39.0%; 95% CI 4.5–73.5%; Wilcoxon–Mann–Whitney test; N = 14; p = 0.03) with similar sensitivity (difference: 14.3%; 95% CI − 21.7 to 50.3%; Wilcoxon–Mann–Whitney test; N = 14; p = 0.42) compared to static thresholds. In some models, training on as little as 10 min of SEEG data yielded robust detection. Cross-testing experiments reduced PPV (difference: 56.5%; 95% CI 25.8–87.3%; Wilcoxon–Mann–Whitney test; N = 14; p = 0.002), while multimodal detection significantly improved sensitivity (difference: 25.0%; 95% CI 0.2–49.9%; Wilcoxon–Mann–Whitney test; N = 14; p < 0.05). Self-supervised dynamic thresholding improved the efficacy of real-time seizure predictions. Multimodal models demonstrated potential to improve detection. These findings are promising for future deployment in epilepsy monitoring units to enable real-time seizure detection without annotated data and only minimal training time in individual patients.
Collapse
Affiliation(s)
- Michael L Martini
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Aly A Valliani
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Claire Sun
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.,Department of Neurosciences, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave Levy Place, New York, NY, 10029, USA
| | - Anthony B Costa
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Shan Zhao
- Department of Anesthesiology, Icahn School of Medicine At Mount Sinai, New York, NY, 10029, USA
| | - Fedor Panov
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Saadi Ghatan
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Kanaka Rajan
- Department of Neurosciences, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave Levy Place, New York, NY, 10029, USA.
| | - Eric Karl Oermann
- Department of Neurosurgery, New York University Langone Medical Center, New York University, Skirball, Suite 8S, 530 First Avenue, New York, NY, 10016, USA. .,Department of Radiology, New York University Langone Medical Center, New York, NY, 10016, USA. .,NYU Center for Data Science, New York University, New York, NY, 10011, USA.
| |
Collapse
|
26
|
Ruiz Marín M, Villegas Martínez I, Rodríguez Bermúdez G, Porfiri M. Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings. iScience 2021; 24:101997. [PMID: 33490905 PMCID: PMC7811137 DOI: 10.1016/j.isci.2020.101997] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 10/23/2020] [Accepted: 12/23/2020] [Indexed: 11/23/2022] Open
Abstract
Automated seizure detection in long-term video-EEG recordings is far from being integrated into common clinical practice. Here, we leverage classical and state-of-the-art complexity measures to robustly and automatically detect seizures from scalp recordings. Brain activity is scored through eight features, encompassing traditional time domain and novel measures of recurrence. A binary classification algorithm tailored to treat unbalanced dataset is used to determine whether a time window is ictal or non-ictal from its features. The application of the algorithm on a cohort of ten adult patients with focal refractory epilepsy indicates sensitivity, specificity, and accuracy of 90%, along with a true alarm rate of 95% and less than four false alarms per day. The proposed approach emphasizes ictal patterns against noisy background without the need of data preprocessing. Finally, we benchmark our approach against previous studies on two publicly available datasets, demonstrating the good performance of our algorithm. Complexity measures are formulated to enhance classical time-domain statistics of EEG The detection algorithm does not need ad-hoc data preprocessing to address artifacts Focal seizures are detected 95% of the time with less than four false alarms per day The approach offers a visual representation of a seizure as a time-evolving network
Collapse
Affiliation(s)
- Manuel Ruiz Marín
- Department of Quantitative Methods, Law and Modern Languages, Technical University of Cartagena (UPCT), Cartagena, Murcia 30201, Spain
- Bio-Health Institute (IMIB-Arrixaca), Health Science Campus, Murcia, CP 30120, Spain
- Corresponding author
| | - Irene Villegas Martínez
- Department of Projects and Innovation, Health Service of Murcia (SMS), Murcia, Spain
- Bio-Health Institute (IMIB-Arrixaca), Health Science Campus, Murcia, CP 30120, Spain
- Corresponding author
| | | | - Maurizio Porfiri
- Department of Quantitative Methods, Law and Modern Languages, Technical University of Cartagena (UPCT), Cartagena, Murcia 30201, Spain
- Department of Mechanical and Aerospace Engineering, and Department of Biomedical Engineering New York University Tandon School of Engineering (NYU), Brooklyn, NY, USA
| |
Collapse
|
27
|
Falsaperla R, Vitaliti G, Marino SD, Praticò AD, Mailo J, Spatuzza M, Cilio MR, Foti R, Ruggieri M. Graph theory in paediatric epilepsy: A systematic review. DIALOGUES IN CLINICAL NEUROSCIENCE 2021; 23:3-13. [PMID: 35860177 PMCID: PMC9286734 DOI: 10.1080/19585969.2022.2043128] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Graph theoretical studies have been designed to investigate network topologies during life. Network science and graph theory methods may contribute to a better understanding of brain function, both normal and abnormal, throughout developmental stages. The degree to which childhood epilepsies exert a significant effect on brain network organisation and cognition remains unclear. The hypothesis suggests that the formation of abnormal networks associated with epileptogenesis early in life causes a disruption in normal brain network development and cognition, reflecting abnormalities in later life. Neurological diseases with onset during critical stages of brain maturation, including childhood epilepsy, may threaten this orderly neurodevelopmental process. According to the hypothesis that the formation of abnormal networks associated with epileptogenesis in early life causes a disruption in normal brain network development, it is then mandatory to perform a proper examination of children with new-onset epilepsy early in the disease course and a deep study of their brain network organisation over time. In regards, graph theoretical analysis could add more information. In order to facilitate further development of graph theory in childhood, we performed a systematic review to describe its application in functional dynamic connectivity using electroencephalographic (EEG) analysis, focussing on paediatric epilepsy.
Collapse
Affiliation(s)
- Raffaele Falsaperla
- Neonatal Intensive Care Unit, San Marco Hospital, University Hospital Policlinico “G. Rodolico-San Marco", Catania, Italy
- Unit of Pediatrics and Pediatric Emergency, University Hospital Policlinico “G. Rodolico-San Marco", Catania, Italy
| | - Giovanna Vitaliti
- Department of Medical Sciences, Unit of Pediatrics, University of Ferrara, Ferrara, Italy
| | - Simona Domenica Marino
- Unit of Pediatrics and Pediatric Emergency, University Hospital Policlinico “G. Rodolico-San Marco", Catania, Italy
| | - Andrea Domenico Praticò
- Unit of Rare Diseases of the Nervous System in Childhood, Department of Clinical and Experimental Medicine, Section of Pediatrics and Child Neuropsychiatry, University of Catania, Catania, Italy
| | - Janette Mailo
- Division of Pediatric Neurology, University of Alberta, Stollery Children’s Hospital, Edmonton, Alberta, Canada
| | - Michela Spatuzza
- National Council of Research, Institute for Biomedical Research and Innovation (IRIB), Unit of Catania, Catania, Italy
| | - Maria Roberta Cilio
- Institute for Experimental and Clinical Research, Catholic University of Leuven, Brussels, Belgium
| | - Rosario Foti
- Department Chief of Rheumatology Unit, San Marco Hospital, University Hospital Policlinico “G. Rodolico-San Marco", Catania, Italy
| | - Martino Ruggieri
- Unit of Rare Diseases of the Nervous System in Childhood, Department of Clinical and Experimental Medicine, Section of Pediatrics and Child Neuropsychiatry, University of Catania, Catania, Italy
| |
Collapse
|