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EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review

Computational Intelligence and Neuroscience 2022年 2022卷 6486570页

作者： Ahmad, Ijaz Wang, Xin Zhu, Mingxing Wang, Cheng Pi, Yao Khan, Javed Ali Khan, Siyab Samuel, Oluwarotimi Williams Chen, Shixiong Li, Guanglin CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Shenzhen China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China School of Biomedical Engineering Sun Yat-Sen University Guangzhou China Department of Software Engineering University of Science and Technology Khyber Pakhtunkhwa Bannu Pakistan Institute of Computer Science and Information Technology The University of Agriculture Khyber Pakhtunkhwa Peshawar Pakistan

Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward developing novel and efficient technology for epileptic seizure management, recent diagnostic approaches have focused on developing machine/deep learning model (ML/DL)-based electroencephalogram (EEG) methods. Importantly, EEG's noninvasiveness and ability to offer repeated patterns of epileptic-related electrophysiological information have motivated the development of varied ML/DL algorithms for epileptic seizure diagnosis in the recent years. However, EEG's low amplitude and nonstationary characteristics make it difficult for existing ML/DL models to achieve a consistent and satisfactory diagnosis outcome, especially in clinical settings, where environmental factors could hardly be avoided. Though several recent works have explored the use of EEG-based ML/DL methods and statistical feature for seizure diagnosis, it is unclear what the advantages and limitations of these works are, which might preclude the advancement of research and development in the field of epileptic seizure diagnosis and appropriate criteria for selecting ML/DL models and statistical feature extraction methods for EEG-based epileptic seizure diagnosis. Therefore, this paper attempts to bridge this research gap by conducting an extensive systematic review on the recent developments of EEG-based ML/DL technologies for epileptic seizure diagnosis. In the review, current development in seizure diagnosis, various statistical feature extraction methods, ML/DL models, their performances, limitations, and core challenges as applied in EEG-based epileptic seizure diagnosis were meticulously reviewed and compared. In addition, proper criteria for selecting appropriate and efficient feature extraction techniques and ML/DL models for epileptic seizure diagnosis were also discussed. Findings from this study will aid researchers in deciding the most efficient ML/DL m

关键词： Electroencephalography

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2020 Annual Conference of the Prognostics and Health Management Society, PHM 2020

作者： Hendrickx, Kilian Meert, Wannes Cornelis, Bram Gryllias, Konstantinos Davis, Jesse Siemens Digital Industries Software Interleuvenlaan 68 Leuven3001 Belgium KU Leuven Department of Computer Science Celestijnenlaan 200A box 2402 Leuven3001 Belgium KU Leuven Department of Mechanical Engineering Celestijnenlaan 300 Leuven3001 Belgium Dynamics of Mechanical and Mechatronic Systems Flanders Make Belgium

ISBN: (纸本)9781936263059

Monitoring machines and early fault detection reduces production downtime, repair costs, and human casualties. Traditionally, machine condition monitoring requires performing a time-consuming manual analysis to find indicators for each potential fault. Moreover, these approaches often only focus on a single machine, while many industrial applications involve monitoring a fleet of machines. In such applications, it is often safe to assume that the majority of machines are in a healthy state. In comparable operating states, the behavior of these healthy machines is similar and any deviating machine is thus likely to be faulty. Previously, we proposed a fleet-based anomaly framework that can assess the health status of each machine in the fleet by detecting these deviations. It groups together similarly behaving machines and assumes that the healthy ones form the largest group and assigns an anomaly score to each machine based on the size of the group it belongs to. In this work, we propose a similarity-based anomaly score that offers multiple benefits over the cluster-based anomaly score. First, this score better represents the severity of a machine fault. Second, it allows to assess the health status of individual machines instead of machine groups. Finally, using similarities provides more nuanced insights in a machine's health status, especially for gradual degrading machines. Experiments show that the similarity-based anomaly score is superior to the cluster-based approach. © 2020 Prognostics and Health Management Society. All rights reserved.

关键词： Condition monitoring

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A Unified Graph-Theoretic Framework for Free-Fermion Solvability

arXiv

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arXiv 2023年

作者： Chapman, Adrian Elman, Samuel J. Mann, Ryan L. Department of Materials University of Oxford Parks Road OxfordOX1 3PH United Kingdom Centre for Quantum Software and Information School of Computer Science Faculty of Engineering & Information Technology University of Technology Sydney NSW 2007 Australia Centre for Engineered Quantum Systems School of Physics The University of Sydney Sydney Australia Centre for Quantum Computation and Communication Technology Australia School of Mathematics University of Bristol BristolBS8 1UG United Kingdom

We show that a quantum spin system has an exact description by non-interacting fermions if its frustration graph is claw-free and contains a simplicial clique. The frustration graph of a spin model captures the pairwise anticommutation relations between Pauli terms of its Hamiltonian in a given basis. This result captures a vast family of known free-fermion solutions. In previous work, it was shown that a free-fermion solution exists if the frustration graph is either a line graph, or (even-hole, claw)-free. The former case generalizes the celebrated Jordan-Wigner transformation and includes the exact solution to the Kitaev honeycomb model. The latter case generalizes a non-local solution to the four-fermion model given by Fendley. Our characterization unifies these two approaches, extending generalized Jordan-Wigner solutions to the non-local setting and generalizing the four-fermion solution to models of arbitrary spatial dimension. Our key technical insight is the identification of a class of cycle symmetries for all models with claw-free frustration graphs. We prove that these symmetries commute, and this allows us to apply Fendley's solution method to each symmetric subspace independently. Finally, we give a physical description of the fermion modes in terms of operators generated by repeated commutation with the Hamiltonian. This connects our framework to the developing body of work on operator Krylov subspaces. Our results deepen the connection between many-body physics and the mathematical theory of claw-free graphs. Copyright © 2023, The Authors. All rights reserved.

关键词： Hamiltonians

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TWIST-GAN: Towards wavelet transform and transferred GAN for spatio-temporal single image super resolution

arXiv

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arXiv 2021年

作者： Dharejo, Fayaz Ali Deeba, Farah Zhou, Yuanchun Das, Bhagwan Jatoi, Munsif Ali Zawish, Muhammad Du, Yi Wang, Xuezhi Computer Network Information Center Chinese Academy of Sciences University of Chinese Academy of Sciences Bejing100190 China Department of Electronic Engineering Quaid-e-Awam University Engineering Science and Technology Nawabshah Pakistan Department of Biomedical Engineering Salim Habib University Karachi Pakistan Telecommunication Software and Systems Group Waterford Institute of Technology Rono-Hills Waterford Ireland

Single Image Super-resolution (SISR) produces high-resolution images with fine spatial resolutions from a remotely sensed image with low spatial resolution. Recently, deep learning and generative adversarial networks (GANs) have made breakthroughs for the challenging task of single image super-resolution (SISR). However, the generated image still suffers from undesirable artifacts such as, the absence of texture-feature representation and high-frequency information. We propose a frequency domain-based spatio-temporal remote sensing single image super-resolution technique to reconstruct the HR image combined with generative adversarial networks (GANs) on various frequency bands (TWIST-GAN).We have introduced a new method incorporating Wavelet Transform (WT) characteristics and transferred generative adversarial network. The LR image has been split into various frequency bands by using theWT, whereas, the transfer generative adversarial network predicts high-frequency components via a proposed architecture. Finally, the inverse transfer of wavelets produces a reconstructed image with super-resolution. The model is first trained on an external DIV2 K dataset and validated with the UC Merceed Landsat remote sensing dataset and Set14 with each image size of 256x256. Following that, transferred GANs are used to process spatio-temporal remote sensing images in order to minimize computation cost differences and improve texture information. The findings are compared qualitatively and qualitatively with the current state-of-art approaches. In addition, we saved about 43% of the GPU memory during training and accelerated the execution of our simplified version by eliminating batch normalization layers. © 2021, CC BY.

关键词： Wavelet transforms

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Approximate String Matching for DNS Anomaly Detection 12th

Approximate String Matching for DNS Anomaly Detection

引用

12th International Conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage, SpaCCS 2019

作者： Mateless, Roni Segal, Michael Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva84105 Israel School of Electrical and Computer Engineering Department of Communication Systems Engineering Ben-Gurion University of the Negev Beer-Sheva84105 Israel

ISBN: (纸本)9783030249069

In this paper we propose a novel approach to identify anomalies in DNS traffic. The traffic time-points data is transformed to a string, which is used by new fast approximate string matching algorithm to detect anomalies. Our approach is generic in its nature and allows fast adaptation to different types of traffic. We evaluate the approach on a large public dataset of DNS traffic based on 10 days, discovering more than order of magnitude DNS attacks in comparison to auto-regression as a baseline. Moreover, the additional comparison has been made including other common regressors such as Linear Regression, Lasso, Random Forest and KNN, all of them showing the superiority of our approach. © Springer Nature Switzerland AG 2019.

关键词： Anomaly detection

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A Self-adaptive PSO-Based Dynamic Scheduling Method on Hierarchical Cloud Computing 1

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9th EAI International Conference on Cloud Computing, CloudComp 2019 and the 4th EAI International Conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2019

作者： Meng, Shunmei Huang, Weijia Xu, Xiaolong Li, Qianmu Dou, Wanchun Liu, Bowen Department of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9783030485139

ISBN: (纸本)9783030485122

Edge computing has been envisioned as an emerging and prospective computing paradigm for its advantage of low latency, which uses local resources. However, the edge resources are usually limited and could not meet end-users’ diversified requirement. Cloud computing paradigm could provide scalable and centralized resources with high computational capabilities, but it has latency issues. Thus it is suggested to combine both computing paradigms together to improve the performance of mobile applications. In this paper, we propose a self-adaptive dynamic scheduling approach based on hierarchical heterogeneous clouds. Our scheduling mechanism considers not only schedule planning but also dynamic scheduling on heterogeneous clouds. Firstly, a self-adaptive scheduling mechanism based on a meta-heuristic optimization algorithm, PSO (Particle Swarm Optimization), is presented for schedule planning. Then a dynamic scheduling mechanism on dynamic partial workflow model is proposed for dynamic optimization during the execution. Finally, external experiments compared with other methods are conducted to demonstrate the effectiveness of our proposal. © 2020, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Mobile edge computing

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Study of Block Chain and its Impact on Internet of Health Things (IoHT):Challenges and Opportunities

Study of Block Chain and its Impact on Internet of Health Th...

引用

Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Muhammad Raza Naqvi Muhammad Aslam Muhammad Waseem Iqbal Syed Khuram Shahzad Marium Malik Muhammad Usman Tahir Department of Computer Science The Superior College Lahore Pakistan Department of Computer Science University of Engineering & Technology Lahore Pakistan Department of Software Engineering The Superior College Lahore Pakistan Department of Informatics & Systems University of Management and Technology Lahore Pakistan

ISBN: (数字)9781728193526

ISBN: (纸本)9781728193533

Blockchain has been making some vital progress in these days because of its secure and amiable structure. It is a distributed and decentralized database that deals with transactions. The blockchain is also emerging in the healthcare sector rapidly. Healthcare has now become a prestigious part of our life as it is making life-changing innovations day by day whether it is about artificial intelligence influence in the diagnostic system, expert system, patient's data storage, retrieval, security, and any other healthcare smart services. Smart health data is the most important ingredient to help us in solving our healthcare needs. Many researchers are now emerging blockchain in smart health because of its secure, flexible, and reliable architecture. In this paper, the major progress made in implementing the blockchain and its vulnerabilities are discussed in the area of smart health. The major focus is on the issues of patient's electronic health record, retrieval, security, and interoperability in Internet of Health Things (IoHT).

关键词： Interoperability Contracts computer science

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Importance of Big Data in Precision and Personalized Medicine

Importance of Big Data in Precision and Personalized Medicin...

引用

Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Muhammad Raza Naqvi Muhammad Arfan Jaffar Muhammad Aslam Syed Khuram Shahzad Muhammad Waseem Iqbal Amjad Farooq Department of Computer Science The Superior College Lahore Pakistan Department of Software Engineering The Superior College Lahore Pakistan Department of Computer Science University of Engineering & Technology Lahore Pakistan Department of Informatics & Systems University of Management and Technology Lahore Pakistan

ISBN: (数字)9781728193526

ISBN: (纸本)9781728193533

The rapidly increasing adaptation of Big data technologies in biomedicine, has introduced a revolution in and medical research practice. Trending high-throughput data analysis techniques, have converted the appearance of the biological system to acquire idolization methods for complicated diseases. Majority of the acquired Big-data models govern the materialization of illustrating medicine. This transformation aims at quantification of the period of P4 medicine that will then progressively be more predictive, personalized, pre-emptive, and participatory. It layouts a track to modernize antiseptic methods for the patient's concern center. P4 medicine besides being a scientific face of systems medicine has two highlighted purposes first of which is to evaluate wellness, while the other is, to identify and expose disease. Patients are major operators in the cognizance of P4 medicine as they directly get engaged with a medically familiar network that helps them boost their health. This article will discuss the maturity in big data planning and correlated challenges in biomedicine.

关键词： Big Data Genomics Bioinformatics Cancer Medical diagnostic imaging

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A Consensus Privacy Metrics Framework for Synthetic Data

arXiv

引用

arXiv 2025年

作者： Pilgram, Lisa Dankar, Fida K. Drechsler, Jörg Elliot, Mark Domingo-Ferrer, Josep Francis, Paul Kantarcioglu, Murat Kong, Linglong Malin, Bradley Muralidhar, Krishnamurty Myles, Puja Prasser, Fabian Raisaro, Jean Louis Yan, Chao El Emam, Khaled School of Epidemiology and Public Health University of Ottawa ON Canada CHEO Research Institute ON Canada Department of Nephrology and Medical Intensive Care Charité – Universitätsmedizin Berlin Berlin Germany Department for Statistical Methods Institute for Employment Research Nuernberg Germany Institute for Statistics Ludwig-Maximilians-Universität Munich Germany Joint Program in Survey Methodology University of Maryland United States The Cathie Marsh Institute Research School of Social Sciences University of Manchester Manchester United Kingdom Department of Computer Engineering and Mathematics Universitat Rovira i Virgili Catalonia Tarragona Spain Max Planck Institute for Software Systems Germany Department of Computer Science Virginia Tech United States Department of Mathematical and Statistical Sciences University of Alberta Alberta Canada Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Biostatistics Vanderbilt University Medical Center NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States Department of Marketing and Supply Chain Management University of Oklahoma Oklahoma United States Medicines and Healthcare products Regulatory Agency London United Kingdom Berlin Institute of Health at Charité Universitätsmedizin Berlin Medical Informatics Group Berlin Germany Biomedical Data Science Center University Hospital Lausanne Lausanne Switzerland

Synthetic data generation is one approach for sharing individual-level data. However, to meet legislative requirements, it is necessary to demonstrate that the individuals’ privacy is adequately protected. There is no consolidated standard for measuring privacy in synthetic data. Through an expert panel and consensus process, we developed a framework for evaluating privacy in synthetic data. Our findings indicate that current similarity metrics fail to measure identity disclosure, and their use is discouraged. For differentially private synthetic data, a privacy budget other than close to zero was not considered interpretable. There was consensus on the importance of membership and attribute disclosure, both of which involve inferring personal information about an individual without necessarily revealing their identity. The resultant framework provides precise recommendations for metrics that address these types of disclosures effectively. Our findings further present specific opportunities for future research that can help with widespread adoption of synthetic data. © 2025, CC BY.

关键词： Differential privacy

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Science of design for societal-scale cyber-physical systems: challenges and opportunities

引用

Cyber-Physical systems 2019年第3期5卷 145-172页

作者： Sztipanovits, Janos Koutsoukos, Xenofon Karsai, Gabor Sastry, Shankar Tomlin, Claire Damm, Werner Fränzle, Martin Rieger, Jochem Pretschner, Alexander Köster, Frank Institute for Software Integrated Systems Vanderbilt University NashvilleTN United States Electrical Engineering and Computer Sciences University of California BerkeleyCA United States Department of Computer Science University of Oldenburg Oldenburg Germany Computer Science Technical University of Munich München Germany Braunschweig Germany

Emerging industrial platforms such as the Internet of Things (IoT), Industrial Internet (II) in the US and Industrie 4.0 in Europe have tremendously accelerated the development of new generations of Cyber-Physical systems (CPS) that integrate humans and human organizations (H-CPS) with physical and computation processes and extend to societal-scale systems such as traffic networks, electric grids, or networks of autonomous systems where control is dynamically shifted between humans and machines. Although such societal-scale CPS can potentially affect many aspect of our lives, significant societal strains have emerged about the new technology trends and their impact on how we live. Emerging tensions extend to regulations, certification, insurance, and other societal constructs that are necessary for the widespread adoption of new technologies. If these systems evolve independently in different parts of the world, they will ‘hard-wire’ the social context in which they are created, making interoperation hard or impossible, decreasing reusability, and narrowing markets for products and services. While impacts of new technology trends on social policies have received attention, the other side of the coin–to make systems adaptable to social policies–is nearly absent from engineering and computer science design practice. This paper focuses on technologies that can be adapted to varying public policies and presents (1) hard problems and technical challenges and (2) some recent research approaches and opportunities. The central goal of this paper is to discuss the challenges and opportunities for constructing H-CPS that can be parameterized by social context. The focus in on three major application domains: connected vehicles, transactive energy systems, and unmanned aerial vehicles. Abbreviations: CPS: Cyber-physical systems;H-CPS: Human-cyber-physical systems;CV: Connected vehicle;II: Industrial Internet;IoT: Internet of Things. © 2019, © 2019 Informa UK Limited, trading a

关键词： Cyber Physical System

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