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SSRN 2023年

作者： Niu, Xiao-Dong Lu, Li-Rong Wang, Jian Li, Xuan Bio-Medical Engineering Department Changzhi Medical College Changzhi046000 China Engineering Research Centre for Intelligent Data Assisted Diagnosis and Treatment in Shanxi Province Changzhi046000 China Shanxi Key Laboratory of Signal Capturing and Processing North University of China Taiyuan030051 China

Analysis of Heart Rate Variability(HRV) can evaluate diseases related to autonomic nervous function. Linear analysis method can't reflect the time-varying characteristics of HRV. Therefore, nonlinear analysis methods, especially multiscale entropy (MSE), have received extensive attention and research. However, the nonstationary of HRV makes MSE with fixed scale unsuitable for comprehensive and accurate analysis of HRV. Here, we combine integral mean mode decomposition (IMMD), an adaptive orthogonal decomposition method of data, with the sample entropy to quantify the complexity of HRV on adaptive multi-scales. We call it adaptive multiscale entropy (AMSE). AMSE analysis of HRV from three types of subjects, Atrial Fibrillation (AF), Congestive Heart Failure (CHF) and Healthy Sinus Rhythm (NSR), shows that the pathological states of AF and CHF reduce the complexity of the heart system on different adaptive scales, and change the adaptive scales of the heart system in different degrees. However, these findings cannot be obtained by MSE method. In addition, three types of HRV can be identified by the scales and complexity of HRV obtained by AMSE: in the 80 samples, the accuracy, sensitivity and specificity of CHF identification are 0.913, 0.900 and 0.925, respectively;The accuracy, sensitivity and specificity of AF recognition are 0.988, 0.975 and 1, respectively, which are superior to linear and MSE methods. In a word, AMSE can comprehensively and accurately assess the complexity of HRV. © 2023, The Authors. All rights reserved.

关键词： Mode decomposition

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Evaluation of Disinfection Methods for Autonomous Mobile Robots Used in Hospital Logistics in Emergency Departments

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The Journal of hospital infection 2025年 162卷 17-25页

作者： Przemysław Kardas Filip Bielec Małgorzata Brauncajs Paweł Lewek Dariusz Timler Ewelina Łojewska Marcello Chiurazzi Neri Niccolò Dei Gastone Ciuti Raquel Juliá Ros Víctor Solaz Estevan Alessia Maccaro Leandro Pecchia Beatriz Merino Alejandro Medrano Thomas Penzel Giuseppe Fico Department of Family Medicine Medical University of Lodz Lodz 90419 Poland. Department of Microbiology and Laboratory Medical Immunology Medical University of Lodz Lodz 90419 Poland. Department of Emergency and Disaster Medicine Medical University of Lodz Lodz 90419 Poland. Office for Research Strategies and Development Medical University of Lodz Lodz 90419 Poland. BioRobotics Institute & Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Pisa 56127 Italy. Robotnik Automation S.L. Paterna Valencia 46980 Spain. Department of Social Sciences University of Naples Federico II Applied Biomedical Signal Processing Intelligent eHealth Laboratory School of Engineering University of Warwick Coventry CV4 7AL UK. Applied Biomedical Signal Processing Intelligent eHealth Laboratory School of Engineering University of Warwick Coventry CV4 7AL UK Department of Engineering University Campus Bio-Medico of Rome Via Alvaro del Portillo 21 Rome 00128 Italy. Life Supporting Technologies Departamento de Tecnología Fotónica y Bioingeniería Universidad Politècnica de Madrid Madrid Spain. Interdisciplinary Center of Sleep Medicine Charité University Medicine Berlin Berlin Germany.

BACKGROUND:Autonomous Mobile Robots (AMRs) have been increasingly used in hospital logistics, particularly in high-risk areas, such as Emergency Departments (EDs), to streamline operations, reduce staff fatigue, and minimize infection risks. However, their effective disinfection remains a critical concern, especially when it comes to solutions aimed at preventing spread of multi-drug-resistant organisms. OBJECTIVE:This study evaluated the microbiological cleanliness and effectiveness of various disinfection methods for AMRs in real-life hospital logistics settings, with a particular focus on their application in EDs. METHODS:The HOSBOT, an AMR designed for hospital logistics, was deployed in a tertiary hospital for two weeks and validated for transport of biological samples. Microbiological contamination was assessed at multiple robot sites before and after disinfection, using two methods, i.e. manual wiping with a standard disinfectant and non-contact fumigation with low temperature vaporized hydrogen peroxide. Contamination levels were evaluated using quantitative and qualitative microbiological techniques, and a threshold of <2.5 colony-forming units/cm for critical environments, recommended by the Centers for Disease Control and Prevention (CDC), was considered as proof of success. RESULTS:Bacterial contamination exceeded thresholds at all sites. Both disinfection methods significantly decreased contamination. Manual wiping reduced bacterial counts below thresholds and eradicated fungal growth, while fumigation was effective for bacterial but not fungal contamination. Fumigation also failed to meet CDC cleanliness standards in hard-to-reach areas. CONCLUSIONS:Both manual wiping and fumigation effectively reduced bacterial contamination, however, wiping showed better results in fungal eradication. Improvements to fumigation methods are necessary, such as application of higher disinfectant concentrations or alternative chemicals. The aforementioned findings not on

关键词： Autonomous Mobile Robots Bacterial contamination Decontamination Disinfection Emergency Department Fungal contamination Hospital logistics Intensive care unit Microbiological cleanliness Robotics

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Pulse waveform key point recognition by wavelet transform for central aortic blood pressure estimation

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Journal of Information and Computational Science 2012年第1期9卷 25-33页

作者： Zheng, Gang Huang, Qingjun Yan, Guodong Dai, Min Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin 300384 China Laboratory of Bio Signal and Intelligent Processing Tianjin University of Technology Tianjin 300384 China

Non invasion estimating of Central Aortic blood Pressure (CAP) is still a hard problem. Through tenyears clinical research plan (CAFE, Conduit Artery Functional Endpoint) on CAP, CAP is a vital sign to evaluate human's vascular condition. It is more valuable than ordinary brachial artery blood pressure. Non invasion estimating of CAP is depended on the key point recognition of radial pulse, especially the peak of tidal wave (left ventricular stopping ejection point, point e). Since someone's point e is not obvious in his/her pulse waveform, orientation of this point has becoming a difficult problem to be tackled. Through deep study on the feature of pulse waveform, an modified wavelet transform is proposed. It is used to recognize point e of pulse waveform by relative displacement between the 4th and 5th scale of a symlet (it is a kind of discrete wavelet transform). After experiments on 16 patients and 20 healthy volunteers, the results support the proposed strategy in recognizing both obvious and unobvious point e of pulse waveform. The CAP can be estimated by the point e accurately, and the results can meet the requirement of clinical diagnosis. © 2012 Binary Information Press January 2012.

关键词： Clinical research

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