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Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013–2023)

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Computers in Biology and Medicine 2024年 172卷 108207-108207页

作者： Gudigar, Anjan Kadri, Nahrizul Adib Raghavendra, U. Samanth, Jyothi Maithri, M. Inamdar, Mahesh Anil Prabhu, Mukund A. Hegde, Ajay Salvi, Massimo Yeong, Chai Hong Barua, Prabal Datta Molinari, Filippo Acharya, U. Rajendra Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Department of Biomedical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia Department of Cardiovascular Technology Manipal College of Health Professions Manipal Academy of Higher Education Manipal576104 India Department of Mechatronics Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Department of Cardiology Kasturba Medical College Manipal Academy of Higher Education Manipal576104 India Manipal Hospitals Karnataka Bengaluru560102 India Biolab PolitoBIOMedLab Department of Electronics and Telecommunications Politecnicodi Torino Turin Italy School of Medicine Faculty of Health and Medical Sciences Taylor's University Subang Jaya47500 Malaysia Cogninet Brain Team Cogninet Australia SydneyNSW2010 Australia Faculty of Business Education Law & Arts University of Southern Queensland ToowoombaQLD4350 Australia Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia School of Mathematics Physics and Computing University of Southern Queensland SpringfieldQLD4300 Australia Centre for Health Research University of Southern Queensland ToowoombaQLD4350 Australia

Artificial Intelligence (AI) techniques are increasingly used in computer-aided diagnostic tools in medicine. These techniques can also help to identify Hypertension (HTN) in its early stage, as it is a global health issue. Automated HTN detection uses socio-demographic, clinical data, and physiological signals. Additionally, signs of secondary HTN can also be identified using various imaging modalities. This systematic review examines related work on automated HTN detection. We identify datasets, techniques, and classifiers used to develop AI models from clinical data, physiological signals, and fused data (a combination of both). Image-based models for assessing secondary HTN are also reviewed. The majority of the studies have primarily utilized single-modality approaches, such as biological signals (e.g., electrocardiography, photoplethysmography), and medical imaging (e.g., magnetic resonance angiography, ultrasound). Surprisingly, only a small portion of the studies (22 out of 122) utilized a multi-modal fusion approach combining data from different sources. Even fewer investigated integrating clinical data, physiological signals, and medical imaging to understand the intricate relationships between these factors. Future research directions are discussed that could build better healthcare systems for early HTN detection through more integrated modeling of multi-modal data sources. © 2024 The Authors

关键词： Medical imaging

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Exploring the etiology of auditory-verbal hallucinations through air- and bone-conducted self-other voice discrimination, a pilot study with preliminary results

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Neuroscience Applied 2024年 3卷

作者： I. Muselimović M. Bagarić J. Vukojević A. Savić P. Brečić L. Jelić P. Orepić University Psychiatric Hospital Vrapče Department for Diagnostics and Intensive care Zagreb Croatia University Psychiatric Hospital Vrapče Department for Affective disorders Zagreb Croatia Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Zagreb Croatia University of Geneva Campus Biotech Geneva Switzerland

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Influence of the variables describing brain signals on the performance of the Naive Bayesian Classifier

Influence of the variables describing brain signals on the p...

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Progress in Applied Electrical engineering (PAEE)

作者： Aleksandra Kawala-Sterniuk Jaroslaw Zygarlicki Adam Lysiak Barbara Grochowicz Mariusz Pelc Waldemar Bauer Dawid Baczkowicz Krzysztof Krecisz Katarzyna A. Dylag Wiktoria Wieczorek Radana Kahankova Radek Martinek Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland School of Computing and Mathematical Sciences University of Greenwich London UK Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland Faculty of Physical Education and Physiotherapy Opole University of Technology Opole Poland St. Louis Children Hospital Krakow Poland Department of Pathophysiology Jagiellonian University in Krakow–Collegium Medicum Krakow Poland Department of Bioinformatics and Telemedicine Jagiellonian University in Krakow–Collegium Medicum Krakow Poland Department of Cybernetics and Biomedical Engineering–FEECS VSB—Technical University Ostrava Ostrava–Poruba Czech Republic

ISBN: (数字)9781665455152

ISBN: (纸本)9781665455169

In this paper various variables describing brain signals such as near-infrared spectroscopy (NIRS) and electroencephalography (EEG) on the Naive Bayesian Classifier were presented. Analysis of biomedical signals is a very challenging task – therefore the authors of this work decided to analyse two types of brain signals – EEG and NIRS, which have different features, character, nature and are differently prone to various artifacts occurrence. The applied classifiers can be applied for various features extraction and pattern comparisons, which is useful in diagnostic processes.

关键词： Electrical engineering Feature extraction Electroencephalography Bayes methods Task analysis Functional near-infrared spectroscopy

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Numerical Simulation of Lowering a Payload into Waves with a Heave Compensation System Using Openfoam

SSRN

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SSRN 2022年

作者： Wang, Kai Ma, Xin Bai, Wei Qian, Ling Lin, Zaibin School of Control Science and Engineering Shandong University Shandong China Centre for Mathematical Modelling and Flow Analysis Department of Computing and Mathematics Manchester Metropolitan University ManchesterM1 5GD United Kingdom School of Engineering University of Aberdeen AberdeenAB24 3UE United Kingdom 17923 Jingshi Road. Shandong Jinan250061 China

In heavy lifting and lowering marine operations, heave compensation is wildly used to decouple the vessel heave motion from the payload and reduce the dynamic cable tension. This paper proposes a novel OpenFOAM-based numerical model to study the process of lowering payloads into waves by an offshore crane with a heave compensation system. First, an elastic cable module and electric winch system are established in a three-dimensional Numerical Wave Tank (NWT) model to achieve the crane vessel lifting and lowering operation. A correction method for the cable spring force is proposed to reduce the calculation error of the cable system. Second, a heave compensation is designed to compensate the vessel motion while taking hydrodynamic forces on payloads during the lowering operation into account. A physical experiment of lowering an aluminum cuboid into a water tank by the crane platform is conducted to verify the developed model. Finally, the process of lowering payloads into waves is simulated to analyze the dynamic character of the cable force and the payload motion using the developed numerical model. A PID controller is applied to stabilize the payload motion by controlling the winch velocity, reducing the dynamic cable tension and the risk of cable slack. © 2022, The Authors. All rights reserved.

关键词： Numerical models

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Asynchronous Activity Detection for Cell-Free Massive MIMO: From Centralized to Distributed Algorithms

arXiv

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arXiv 2022年

作者： Li, Yang Lin, Qingfeng Liu, Ya-Feng Ai, Bo Wu, Yik-Chung Shenzhen Research Institute of Big Data Shenzhen518172 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China

Device activity detection in the emerging cell-free massive multiple-input multiple-output (MIMO) systems has been recognized as a crucial task in machine-type communications, in which multiple access points (APs) jointly identify the active devices from a large number of potential devices based on the received signals. Most of the existing works addressing this problem rely on the impractical assumption that different active devices transmit signals synchronously. However, in practice, synchronization cannot be guaranteed due to the low-cost oscillators, which brings additional discontinuous and nonconvex constraints to the detection problem. To address this challenge, this paper reveals an equivalent reformulation to the asynchronous activity detection problem, which facilitates the development of a centralized algorithm and a distributed algorithm that satisfy the highly nonconvex constraints in a gentle fashion as the iteration number increases, so that the sequence generated by the proposed algorithms can get around bad stationary points. To reduce the capacity requirements of the fronthauls, we further design a communication-efficient accelerated distributed algorithm. Simulation results demonstrate that the proposed centralized and distributed algorithms outperform state-of-the-art approaches, and the proposed accelerated distributed algorithm achieves a close detection performance to that of the centralized algorithm but with a much smaller number of bits to be transmitted on the fronthaul links. Copyright © 2022, The Authors. All rights reserved.

关键词： Internet of things

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Non-Hermitian lattice fermions in the 2D Gross-Neveu-Yukawa model

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Physical Review D 2024年第3期110卷 034502-034502页

作者： Xingyu Guo Chen-Te Ma Hui Zhang Key Laboratory of Atomic and Subatomic Structure and Quantum Control (MOE) Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter Institute of Quantum Matter South China Normal University Department of Physics and Astronomy Iowa State University Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum Matter South China Normal University Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University Asia Pacific Center for Theoretical Physics Pohang University of Science and Technology School of Physics and Telecommunication Engineering South China Normal University The Laboratory for Quantum Gravity and Strings Department of Mathematics and Applied Mathematics University of Cape Town Physics Department Center for Exploration of Energy and Matter Indiana University

We work the lattice fermions and non-Hermitian formulation in the 2D Gross-Neveu-Yukawa (GNY) model and demonstrate the numerical implementation for two flavors by the hybrid Monte Carlo. Our approach has a notable advantage in dealing with chiral symmetry on a lattice by avoiding the Nielsen-Ninomiya theorem, due to the nonsymmetrized finite-difference operator. We restore the hypercubic symmetry by averaging over all possible orientations with the proper continuum limit. Our study is the first simulation for the interacting fermion formulated in a non-Hermitian way. We compare the numerical solution with the one-loop resummation. The resummation results matches with the numerical solution in ⟨ϕ⟩, ⟨ϕ2⟩, ⟨Tr(ψ¯1ψ1+ψ¯2ψ2)/2⟩, and ⟨Tr(ψ¯1ψ1+ψ¯2ψ2)ϕ/2⟩. We also used the one-loop resummation to provide the renormalization group flow and asymptotic safety in the 2D GNY model.

关键词： Nonperturbative effects in field theory Perturbation theory Chiral symmetry Hybrid Monte Carlo algorithm Lattice field theory

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Artificial intelligence in cancer target identification and drug discovery

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Signal Transduction and Targeted Therapy 2022年第6期7卷 1951-1974页

作者： Yujie You Xin Lai Yi Pan Huiru Zheng Julio Vera Suran Liu Senyi Deng Le Zhang College of Computer Science Sichuan UniversityChengdu 610065China Laboratory of Systems Tumor Immunology Friedrich-Alexander-Universität Erlangen-Nürnberg(FAU)and Universitätsklinikum ErlangenErlangen 91052Germany Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesRoom D5131068 Xueyuan AvenueShenzhen University TownShenzhen 518055China School of Computing Ulster UniversityBelfast BT151EDUK Institute of Thoracic Oncology Department of Thoracic SurgeryWest China HospitalSichuan UniversityChengdu 610065China Key Laboratory of Systems Biology Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesChinese Academy of SciencesHangzhou 310024China Key Laboratory of Systems Health Science of Zhejiang Province Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as ***,we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover ***,we describe the scope of artificial intelligence biology analysis for novel anticancer target ***,we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence ***,we showcase the applications of artificial intelligence approaches in cancer target identification and drug *** together,the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer,thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

关键词： drugs cancer drug

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Machine Learning and Computer Vision Techniques in Continuous Beehive Monitoring Applications: A Survey

arXiv

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arXiv 2022年

作者： Bilik, Simon Zemcik, Tomas Kratochvila, Lukas Ricanek, Dominik Richter, Miloslav Zambanini, Sebastian Horak, Karel Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Technická 3058/10 Brno61600 Czech Republic Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Yliopistonkatu 34 Lappeenranta53850 Finland Computer Vision Lab Institute of Visual Computing & Human-Centered Technology Faculty of Informatics TU Wien Favoritenstr. 9/193-1 ViennaA-1040 Austria

Wide use and availability of machine learning and computer vision techniques allows development of relatively complex monitoring systems in many domains. Besides the traditional industrial domain, new applications appears also in biology and agriculture, where they may be used to detect infections, parasites and weeds, but also for automated monitoring and early warning systems. This goes in concordance with the introduction of the easily accessible hardware and development kits such as the Arduino, or RaspberryPi families. In this paper, we survey 50 papers focusing on the methods of automated beehive monitoring using computer vision techniques. Particularly on the pollen and Varroa mite detection together with the bee traffic monitoring. Such systems could also be used for monitoring of honeybee colonies and for the inspection of their health state, which could potentially identify dangerous states before the situation is critical, or to better plan periodic bee colony inspections and therefore save significant costs. Further on, we also include analysis of the research trends in this application field and we outline the possible directions of new development. Our paper is also aimed at veterinary and apidology professionals and experts, who may not be familiar with machine learning to introduce them to its capabilities, hence each family of techniques is prefaced by a brief theoretical introduction and motivation related to its base method. We hope that this paper will inspire other scientists to use machine learning techniques for other applications in beehive monitoring. © 2022, CC BY.

关键词： Computer vision

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Prediction of multi-parameters in the inverse heat conduction problems

Prediction of multi-parameters in the inverse heat conductio...

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2020 International Conference on Physics, Mechanics and Mathematical Science, PMMS 2020

作者： Abdelhamid, Talaat Chen, Rongliang Alam, Mahbub Shenzhen Institutes of Advanced Technology Chinese Academy of Science Shenzhen China Institute for Turbulence-Noise-Vibration Interaction and Control Shenzhen Graduate School Harbin Institute of Technology Shenzhen China Physics and Mathematical Engineering Department Faculty of Electronic Engineering Menoufiya University Menouf Egypt Shenzhen Key Laboratory for Exascale Engineering and Scientific Computing Shenzhen China

This paper studies the prediction of the spatial-dependent-heat transfer coefficient γ(x) and- heat flux q(x), using the modified conjugate gradient method (MCGM). The mathematical formulation of the problem well defined and the existence of the minimizer is investigated. We establish the sensitivity and adjoint equations for computing the gradient with respect to γ(x) and q(x). The proposed algorithm 4.1 is derived for reconstructing γ(x) and q(x) using the MCGM. The numerical experiments are examined to show the efficiency and accuracy of the proposed method. Finally, some conclusions and remarks are given. © Published under licence by IOP Publishing Ltd.

关键词： Conjugate gradient method

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A general framework for lifelong localization and mapping in changing environment

arXiv

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arXiv 2021年

作者： Zhao, Min Guo, Xin Song, Le Qin, Baoxing Shi, Xuesong Lee, Gim Hee Sun, Guanghui Gaussian Robotics Shanghai China Intel Labs China Beijing China Computer Vision and Robotic Perception Lab Department of Computer Science School of Computing National University of Singapore Singapore Singapore Department of Control Science and Engineering Harbin Institute of Technology China

The environment of most real-world scenarios such as malls and supermarkets changes at all times. A prebuilt map that does not account for these changes becomes out-of-date easily. Therefore, it is necessary to have an up-todate model of the environment to facilitate long-term operation of a robot. To this end, this paper presents a general lifelong simultaneous localization and mapping (SLAM) framework. Our framework uses a multiple session map representation, and exploits an efficient map updating strategy that includes map building, pose graph refinement and sparsification. To mitigate the unbounded increase of memory usage, we propose a maptrimming method based on the Chow-Liu maximum-mutualinformation spanning tree. The proposed SLAM framework has been comprehensively validated by over a month of robot deployment in real supermarket environment. Furthermore, we release the dataset collected from the indoor and outdoor changing environment with the hope to accelerate lifelong SLAM research in the community. Our dataset is available at https://***/sanduan168/lifelong-SLAM-dataset. © 2021, CC BY.

关键词： Retail stores

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