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arXiv 2024年

作者： Latif, Ehsan Zhou, Yifan Guo, Shuchen Gao, Yizhu Shi, Lehong Nyaaba, Matthew Lee, Gyeonggeon Zhang, Liang Bewersdorff, Arne Fang, Luyang Yang, Xiantong Zhao, Huaqin Jiang, Hanqi Lu, Haoran Li, Jiaxi Yu, Jichao Wu, Xuansheng You, Weihang Liu, Zhengliang Liu, Vincent Shung Wang, Hui Wu, Zihao Lu, Jin Dou, Fei Ma, Ping Liu, Ninghao Liu, Tianming Zhai, Xiaoming AI4STEM Education Center University of Georgia AthensGA United States National GENIUS Center AthensGA United States School of Computing University of Georgia AthensGA United States Department of Electrical Engineering and Computer Engineering University of Memphis MemphisTN United States Department of Natural Sciences and Science Education National Institute of Education Singapore Singapore School of Social Sciences and Technology Technical University of Munich Munich Germany Department of Statistics University of Georgia AthensGA United States Faculty of Psychology Beijing Normal University Beijing China Second Language Acquisition and Teaching University of Arizona TucsonAZ United States Department of Mathematics Science and Social Studies Education University of Georgia AthensGA United States Department of Workforce Education and Instructional Technology University of Georgia AthensGA United States School of Teacher Education Nanjing Normal University Jiangsu Nanjing China

As artificial intelligence (AI) continues to advance, it demonstrates capabilities comparable to human intelligence, having significant potential to revolutionize education and workforce development. To assess the state-of-art AI capacity, this study provides a comprehensive evaluation of OpenAI o1-preview’s ability to perform higher-order cognitive tasks across 14 distinct dimensions, including critical thinking, systems thinking, computational thinking, design thinking metacognition, data literacy, creative thinking, abstract reasoning, quantitative reasoning, logical reasoning, analogical reasoning, and scientific reasoning. In each dimension, we adopt existing instruments with high reliability and validity, such as the Ennis-Weir Critical Thinking Essay Test and the Biological Systems Thinking Test to systematically assess o1-preview’s higher-order thinking in comparison to human performance. We found overall outperformance of o1-preview for most of the categories: • o1-preview achieved significantly outperform (150%) as compared to humans for systematic thinking, computational thinking, data literacy, creative thinking, scientific reasoning, and abstract reasoning. • For logical reasoning, critical thinking, and quantitative reasoning, o1-preview slightly under-performed by 25% than human such as for logical reasoning. • For analogical reasoning, we found that both human and o1-preview achieve perfect scores for each given task. Nevertheless, we identify o1-preview’s limited capacity in thinking such as abstract reasoning, where humans (i.e., psychology students) perform better than o1-preview, indicating that human oversight remains crucial for tasks requiring high-level abstraction. The findings have broad implications for education, advocating a shift in educational paradigms to emphasize the development of complementary human skills, such as creativity, abstract reasoning, and critical thinking. This study highlights the transformative role of AI in educati

关键词： Systems thinking

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Metal-oxide nanocatalysts for spontaneous sequestration of endocrine-disrupting compounds from wastewater

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Chemosphere 2024年 367卷 143569页

作者： Bamisaye, Abayomi Abati, Shakirudeen Modupe Ige, Ayodeji Rapheal Etafo, Nelson Oshogwue Alli, Yakubu Adekunle Bamidele, Muyideen Olaitan Okon-Akan, Omolabake Abiodun Adegoke, Kayode Adesina Abiola-Kuforiji, Olajumoke T. Idowu, Mopelola Abidemi Bello, Olugbenga Solomon Department of Chemistry Faculty of Natural and Applied Sciences Lead City University Ibadan Nigeria Faculty of Civil Engineering and Environmental Sciences Bialystok University of Technology Wiejska 45E Bialystok15-351 Poland Programa de Posgrado en Ciencia y Tecnología de Materiales Facultad de Ciencias Químicas Universidad Aut'onoma de Coahuila Ing. J. C'ardenas Valdez S/N Republica Coahuila Saltillo25280 Mexico UPR8241 Universit'e de Toulouse UPS INPT Toulouse Cedex 4 ToulouseF-31077 France Department of Chemical Sciences Faculty of Science and Computing Ahman Pategi University Patigi-Kpada Road Kwara State Patigi Nigeria Department of Manufacturing and Materials Engineering Kulliyyah of Engineering International Islamic University Malaysia Jalan Gombak Kuala Lumpur53100 Malaysia Department of Food Research Faculty of Chemical Sciences Universidad Aut'onoma de Coahuila Ing. J. C'ardenas Valdez S/N Republica Coahuila Saltillo25280 Mexico Department of Pure and Applied Chemistry Ladoke Akintola University P. M. B. 4000 Ogbomoso Nigeria Wood and Paper Technology Department Federal College of Forestry Jericho Ibadan Nigeria Department of Industrial Chemistry First Technical University Ibadan Nigeria Department of Molecular Biology and Biotechnology Chrisland University Abeokuta Nigeria Department of Chemistry College of Physical Sciences Federal University of Agriculture Abeokuta Nigeria

The quest for a good life, urbanization, and industrialization have led to the widespread distribution of endocrine-disrupting chemicals (EDCs) in water bodies through anthropogenic activities. This poses an imminent threat to both human and environmental health. In recent years, the utilization of advance materials for the removal of EDCs from wastewater has attracted a lot of attention. Metal-oxide nanocatalysts have emerged as promising candidates due to their high surface area, reactivity, and tunable properties, as well as enhanced surface properties such as mesoporous structures and hierarchical morphologies that allow for increased adsorption capacity, improved photocatalytic activity, and enhanced selectivity towards specific EDCs. As a result, they have shown extraordinary efficacy in removing a wide range of EDCs from aqueous solutions, including pharmaceuticals, agrochemicals, personal care items, and industrial chemicals. This study give insight into the unique physicochemical characteristics of metal-oxide nanocatalysts to effectively and efficiently remove harmful EDCs from wastewater. It also discussed the advances in the synthesis, and properties of metal-oxide nanocatalysts, and insight into understanding the fundamental mechanisms underlying the adsorption and degradation of EDCs on metal-oxide nanocatalysts using advanced characterization techniques such as spectroscopic analysis and electron microscopy. The findings of the study present metal-oxide nanocatalysts as a good candidate for the spontaneous sequestration of EDCs from wastewater is an intriguing approach to mitigating water pollution and safeguarding public health and the environment. © 2024 Elsevier Ltd

关键词： Emerging contaminants Endocrine disruptor chemicals Environment Metal-oxide nanocatalysts Wastewater

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Classification of Lung Cancer by Using Machine Learning Algorithms

Classification of Lung Cancer by Using Machine Learning Algo...

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International Conference on Engineering Technology and their Applications (IICETA)

作者： Joman Al-Tawalbeh Baraa Alshargawi Hiam Alquran Waleed Al-Azzawi Wan Azani Mustafa Ahmed Alkhayyat Department of Biomedical Engineering Jordan University of Science and Technology Irbid Jordan Biomedical Systems and Medical Informatics Engineering Yarmouk University Irbid Jordan Medical technical college Al-Farahidi University Baghdad Iraq Faculty of Electrical Engineering Technology Univerisiti Malaysia Perlis (UniMAP) Pauh Putra Campus Arau Perlis Malaysia Pauh Putra Campus Advanced Computing Centre of Excellence Universiti Malaysia Perlis (UniMAP) Pauh Putra Campus Arau Perlis Malaysia Faculty of Engineering The Islamic University Najaf Iraq

ISBN: (数字)9781665472159

ISBN: (纸本)9781665472166

Due to the structure of cancer cells, where the majority of the cells are overlapped with each other, early diagnosis of lung cancer is a difficult challenge, so the cause of lung cancer remains unknown and prevention is difficult, early discovery of lung cancer is the only approach to cure it. The classification of lung cancer is a crucial process, based on signs that appear on patients; cancers could easily be predicted and treated. This paper uses KNN, SVM, Naïve Bayes and narrow neural network (NNN) classifiers. KNN showed 85.87% accuracy. SVM, Naïve Bayes and NNN showed 92.6%, 90.3% and 90% accuracy respectively. Results came after the fact that among the four classifiers SVM was the most accurate and much better to classify our data.

关键词： Support vector machines Machine learning algorithms Lung cancer Medical treatment Artificial neural networks

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Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age

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Nature Ecology and Evolution 2025年第6期9卷 1031-1042页

作者： Pringle, Stephen Dallimer, Martin Goddard, Mark A. Le Goff, Léni K. Hart, Emma Langdale, Simon J. Fisher, Jessica C. Abad, Sara-Adela Ancrenaz, Marc Angeoletto, Fabio Auat Cheein, Fernando Austen, Gail E. Bailey, Joseph J. Baldock, Katherine C. R. Banin, Lindsay F. Banks-Leite, Cristina Barau, Aliyu S. Bashyal, Reshu Bates, Adam J. Bicknell, Jake E. Bielby, Jon Bosilj, Petra Bush, Emma R. Butler, Simon J. Carpenter, Dan Clements, Christopher F. Cully, Antoine Davies, Kendi F. Deere, Nicolas J. Dodd, Michael Drinkwater, Rosie Driscoll, Don A. Dutilleux, Guillaume Dyrmann, Mads Edwards, David P. Farhadinia, Mohammad S. Faruk, Aisyah Field, Richard Fletcher, Robert J. Foster, Chris W. Fox, Richard Francksen, Richard M. Franco, Aldina M. A. Gainsbury, Alison M. Gardner, Charlie J. Giorgi, Ioanna Griffiths, Richard A. Hamaza, Salua Hanheide, Marc Hayward, Matt W. Hedblom, Marcus Helgason, Thorunn Heon, Sui P. Hughes, Kevin A. Hunt, Edmund R. Ingram, Daniel J. Jackson-Mills, George Jowett, Kelly Keitt, Timothy H. Kloepper, Laura N. Kramer-Schadt, Stephanie Labisko, Jim Labrosse, Frédéric Lawson, Jenna Lecomte, Nicolas de Lima, Ricardo F. Littlewood, Nick A. Marshall, Harry H. Masala, Giovanni L. Maskell, Lindsay C. Matechou, Eleni Mazzolai, Barbara McConnell, Alistair Melbourne, Brett A. Miriyev, Aslan Nana, Eric Djomo Ossola, Alessandro Papworth, Sarah Parr, Catherine L. Payo-Payo, Ana Perry, Gad Pettorelli, Nathalie Pillay, Rajeev Potts, Simon G. Prendergast-Miller, Miranda T. Qie, Lan Rolley-Parnell, Persie Rossiter, Stephen J. Rowcliffe, Marcus Rumble, Heather Sadler, Jon P. Sandom, Christopher J. Sanyal, Asiem Schrodt, Franziska Sethi, Sarab S. Shabrani, Adi Siddall, Robert Smith, Simón C. Snep, Robbert P. H. Soulsbury, Carl D. Stanley, Margaret C. Stephens, Philip A. Stephenson, P.J. Struebig, Matthew J. Studley, Matthew Svátek, Martin Tang, Gilbert Taylor, Nicholas K. Umbers, Kate D. L. Ward, Robert J. White, Patrick J. C. Whittingham, Mark J. Wich, Serge Williams, Christopher D. Yakubu, Ibrahim B. Yoh, Natalie Durrell Institute of Conservation and Ecology (DICE) School of Natural Sciences University of Kent Canterbury United Kingdom Centre for Environmental Policy Imperial College London London United Kingdom Department of Geography and Environmental Sciences Northumbria University Newcastle upon Tyne United Kingdom School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Synthotech Ltd Milner Court Hornbeam Square Harrogate United Kingdom Mechanical Engineering Department University College London London United Kingdom HUTAN SWD Kota Kinabalu Malaysia Programa de Pós-Graduação em Gestão e Tecnologia Ambiental da Universidade Federal de Rondonópolis Rondonópolis Brazil Department of Engineering Harper Adams University Newport United Kingdom Applied Ecology Research Group School of Life Sciences Anglia Ruskin University Cambridge United Kingdom Centre for Ecology and Hydrology Penicuik United Kingdom Department of Life Sciences Imperial College London Silwood Park Campus Ascot United Kingdom Department of Urban and Regional Planning Faculty of Earth and Environmental Sciences Bayero University Kano Nigeria Greenhood Nepal Kathmandu Nepal Animal Rural & Environmental Sciences Nottingham Trent University Nottinghamshire United Kingdom School of Biological and Environmental Sciences Liverpool John Moores University Liverpool United Kingdom Lincoln Centre for Autonomous Systems University of Lincoln Lincoln United Kingdom Royal Botanic Garden Edinburgh Edinburgh United Kingdom School of Biological Sciences University of East Anglia Norwich Research Park Norwich United Kingdom Digital Ecology Limited Bristol United Kingdom School of Biological Sciences University of Bristol Bristol United Kingdom Department of Computing Imperial College London London United Kingdom Department of Ecology and Evolutionary Biology University of Colorado Boulder CO United States Faculty of Science Technology Engineering

With biodiversity loss escalating globally, a step change is needed in our capacity to accurately monitor species populations across ecosystems. Robotic and autonomous systems (RAS) offer technological solutions that may substantially advance terrestrial biodiversity monitoring, but this potential is yet to be considered systematically. We used a modified Delphi technique to synthesize knowledge from 98 biodiversity experts and 31 RAS experts, who identified the major methodological barriers that currently hinder monitoring, and explored the opportunities and challenges that RAS offer in overcoming these barriers. Biodiversity experts identified four barrier categories: site access, species and individual identification, data handling and storage, and power and network availability. Robotics experts highlighted technologies that could overcome these barriers and identified the developments needed to facilitate RAS-based autonomous biodiversity monitoring. Some existing RAS could be optimized relatively easily to survey species but would require development to be suitable for monitoring of more ‘difficult’ taxa and robust enough to work under uncontrolled conditions within ecosystems. Other nascent technologies (for instance, new sensors and biodegradable robots) need accelerated research. Overall, it was felt that RAS could lead to major progress in monitoring of terrestrial biodiversity by supplementing rather than supplanting existing methods. Transdisciplinarity needs to be fostered between biodiversity and RAS experts so that future ideas and technologies can be codeveloped effectively. © The Author(s) 2025.

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The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue

arXiv

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arXiv 2023年

作者： Weitz, Philippe Valkonen, Masi Solorzano, Leslie Carr, Circe Kartasalo, Kimmo Boissin, Constance Koivukoski, Sonja Kuusela, Aino Rasic, Dusan Feng, Yanbo Pouplier, Sandra Sinius Sharma, Abhinav Eriksson, Kajsa Ledesma Robertson, Stephanie Marzahl, Christian Gatenbee, Chandler D. Anderson, Alexander R.A. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Budelmann, Daniel Weiss, Nick Heldmann, Stefan Lotz, Johannes Wolterink, Jelmer M. De Santi, Bruno Patil, Abhijeet Sethi, Amit Kondo, Satoshi Kasai, Satoshi Hirasawa, Kousuke Farrokh, Mahtab Kumar, Neeraj Greiner, Russell Latonen, Leena Laenkholm, Anne-Vibeke Hartman, Johan Ruusuvuori, Pekka Rantalainen, Mattias Department of Medical Epidemiology and Biostatistics Karolinska Insitutet Stockholm Sweden Institute of Biomedicine University of Turku Turku Finland Institute of Biomedicine University of Eastern Finland Kuopio Finland Department of Surgical Pathology Zealand University Hospital Roskilde Denmark Department of Oncology and Pathology Karolinska Institutet Stockholm Sweden Gestalt Diagnostics Spokane United States Department of Integrated Mathematical Oncology Moffitt Cancer Center Tampa United States Informatics Institute University of Applied Sciences Western Switzerland Switzerland Department of Measurement and Electronics Agh University of Kraków Poland Department of Computer Science University of Geneva Geneva Switzerland Department of Neuroscience University of Padova Italy Medical Faculty University of Geneva Switzerland Fraunhofer Institute for Digital Medicine Mevis Lübeck Germany Department of Applied Mathematics Technical Medical Centre University of Twente Enschede Netherlands Multimodality Medical Imaging Technical Medical Centre University of Twente Enschede Netherlands Department of Electrical Engineering Indian Institute of Technology Bombay India Graduate School of Engineering Muroran Institute of Technology Hokkaido Japan Faculty of Medical Technology Niigata University of Health and Welfare Niigata Japan Forxai Business Operations Konica Minolta Inc. Osaka Japan Department of Computing Science University of Alberta EdmontonAB Canada Alberta Machine Intelligence Institute Edmonton Canada MedTechLabs BioClinicum Karolinska University Hospital Stockholm Sweden Faculty of Medicine and Health Technology Tampere University Tampere Finland

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results establish the current state-of-the-art in WSI registration and guide researchers in selecting and developing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Tissue

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Characterization of State-Preparation Uncertainty in Quantum Key Distribution

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Physical Review Applied 2023年第1期19卷 014048-014048页

作者： Anqi Huang Akihiro Mizutani Hoi-Kwong Lo Vadim Makarov Kiyoshi Tamaki Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha 410073 People’s Republic of China Graduate School of Engineering Science Osaka University Toyonaka Osaka 560-8531 Japan Department of Electrical & Computer Engineering and Department of Physics Centre for Quantum Information and Quantum Control (CQIQC) University of Toronto Toronto Ontario M5S 3G4 Canada Department of Physics University of Hong Kong Pokfulam Hong Kong Quantum Bridge Technologies Inc. 100 College Street Toronto Ontario M5G 1L5 Canada Russian Quantum Center Skolkovo Moscow 121205 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale and CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai 201315 People’s Republic of China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow 119049 Russia Faculty of Engineering University of Toyama Gofuku 3190 Toyama 930-8555 Japan

To achieve secure quantum key distribution, all imperfections in the source unit must be incorporated in a security proof and measured in the lab. Here we perform a proof-of-principle demonstration of the experimental techniques for characterizing the source phase and intensity fluctuation in commercial quantum key distribution systems. When we apply the measured phase-fluctuation intervals to the security proof that takes into account fluctuations in the state preparation, it predicts a key distribution distance of over 100km of fiber. The measured intensity fluctuation intervals are, however, so large that the proof predicts zero key, indicating a source improvement may be needed. Our characterization methods pave the way for a future certification standard.

关键词： Fluctuations & noise Optical quantum information processing Quantum cryptography Quantum engineering Quantum state engineering

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Design of a Compact UWB SISO Antenna for Enhanced Wireless Communication Systems

Design of a Compact UWB SISO Antenna for Enhanced Wireless C...

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Advances in Electrical and Communication Technologies (ICAECOT), International Conference on

作者： Aissa Salhi Seifeddine Maouni Issa Elfergani Chemseddine Zebiri Jonathan Rodriguez Felipe Gil-Castiñeira Younes Alfitorey Mousa Atul Varshney EVOTEL informatica Lugo España DocTIC Escola de Enxeñaría de Telecomunicación Universidade de Vigo Espaóa Vigo Espaóa Instituto de Telecomunicações Campus Universitário de Santiago Aveiro Portugal Dept. of Electronic Ferhat LEPCI Laboratory Abbas University Setif Algeria Faculty of Computing Engineering and Science University of South Wales Pontypridd UK AtlanTTic Research Centre for Telecommunications Technologies University of Vigo Spain Technical University of Kosice Letna 1/9 040 01 Košice Slovakia AECE Department FET Gurukula Kangri (Deemed to be University) Haridwar Uttarakhand India

ISBN: (数字)9798350353754

ISBN: (纸本)9798350353761

This study presents a compact ultra-wideband UWB SISO antenna designed for UWB communication systems. The primary objective is to develop a compact antenna system ($20 \mathrm{~mm} \times 15 \mathrm{~mm}$) capable of operation across the entire UWB frequency range through strategic integration of metamaterials, the proposed single-input single-output (SISO) antenna achieves an extended impedance bandwidth from 3.05 GHz to 15 GHz, with significant self-isolation exceeding -20 dB in the UWB spectrum. Simulation results affirm the antenna’s operational effectiveness, showcasing reliable functionality and optimal performance metrics.

关键词： Measurement Simulation SISO Ultra wideband communication Bandwidth Ultra wideband antennas Metamaterials Reliability Impedance Antenna arrays

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Some results on linear nabla Riemann-Liouville fractional difference equations

Some results on linear nabla Riemann-Liouville fractional di...

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作者： The Anh, Pham Babiarz, Artur Czornik, Adam Niezabitowski, Michal Siegmund, Stefan Department of Mathematics Le Quy Don Technical University 236 Hoang Quoc Viet Hanoi Viet Nam Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Faculty of Mathematics Center for Dynamics Technische Universität Dresden Zellescher Weg 12-14 Dresden01069 Germany

In this paper, we establish some criteria for boundedness, stability properties, and separation of solutions of autonomous nonlinear nabla Riemann-Liouville scalar fractional difference equations. To derive these results, we prove the variation of constants formula for nabla Riemann-Liouville fractional difference equations. © 2020 John Wiley & Sons, Ltd.

关键词： Difference equations

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Perturbations and Phase Transitions in Swarm Optimization Algorithms

arXiv

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arXiv 2025年

作者： Vantuch, Tomáš Zelinka, Ivan Adamatzky, Andrew Marwan, Norbert Center ENET Technical University of Ostrava Czech Republic Modeling Evolutionary Algorithms Simulation and Artificial Intelligence Faculty of Electrical & Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Computer Science Technical University of Ostrava Czech Republic Unconventional Computing Lab UWE Bristol United Kingdom Leibniz Association Potsdam Germany

Natural systems often exhibit chaotic behavior in their space-time evolution. Systems transiting between chaos and order manifest a potential to compute, as shown with cellular automata and artificial neural networks. We demonstrate that swarm optimization algorithms also exhibit transitions from chaos, analogous to a motion of gas molecules, when particles explore solution space disorderly, to order, when particles follow a leader, similar to molecules propagating along diffusion gradients in liquid solutions of reagents. We analyze these ‘phase-like’ transitions in swarm optimization algorithms using recurrence quantification analysis and Lempel-Ziv complexity estimation. We demonstrate that converging iterations of the optimization algorithms are statistically different from non-converging ones in a view of applied chaos, complexity and predictability estimating indicators. An identification of a key factor responsible for the intensity of their phase transition is the main contribution of this paper. We examined an optimization as a process with three variable factors – an algorithm, number generator and optimization function. More than 9.000 executions of the optimization algorithm revealed that the nature of an applied algorithm itself is the main source of the phase transitions. Some of the algorithms exhibit larger transition-shifting behavior while others perform rather transition-steady computing. These findings might be important for future extensions of these algorithms. © 2025, CC BY-NC-ND.

关键词： Cellular automata

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A library of quantitative markers of seizure severity

arXiv

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arXiv 2022年

作者： Gascoigne, Sarah J. Waldmann, Leonard Schroeder, Gabrielle M. Panagiotopoulou, Mariella Blickwedel, Jess Chowdhury, Fahmida Cronie, Alison Diehl, Beate Duncan, John S. Falconer, Jennifer Faulder, Ryan Guan, Yu Leach, Veronica Livingstone, Shona Papasavvas, Christoforos Thomas, Rhys H. Wilson, Kevin Taylor, Peter N. Wang, Yujiang Interdisciplinary Computing and Complex BioSystems Group School of Computing Newcastle University Newcastle upon Tyne United Kingdom Faculty of Medical Sciences Newcastle University United Kingdom UCL Queen Square Institute of Neurology Queen Square London United Kingdom NHS Greater Glasgow and Clyde Glasgow United Kingdom School of Mathematics Statistics & Physics Newcastle University United Kingdom Technical University Munich Munich Germany Department of Computer Science University of Warwick Warwick United Kingdom Urban Sciences Building 1 Science Square Newcastle upon TyneNE4 5TG United Kingdom

Objective: Understanding fluctuations in seizure severity within individuals is important for determining treatment outcomes and responses to therapy, as well as assessing novel treatments for epilepsy. Current methods for grading seizure severity rely on qualitative interpretations from patients and clinicians. Quantitative measures of seizure severity would complement existing approaches, for electroencephalographic (EEG) monitoring, outcome monitoring, and seizure prediction. Therefore, we developed a library of quantitative EEG markers that assess the spread and intensity of abnormal electrical activity during and after seizures. Methods: We analysed intracranial EEG (iEEG) recordings of 1009 seizures from 63 patients. For each seizure we computed 16 markers of seizure severity that capture the signal magnitude, spread, duration, and post-ictal suppression of seizures. Results: Quantitative EEG markers of seizure severity distinguished focal vs. subclinical seizures across patients. In individual patients 53% had a moderate to large difference (ranksum r > 0.3, p Copyright © 2022, The Authors. All rights reserved.

关键词： Grading

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