BioMedical Engineering OnLine | Home page

Time 2020-11-13 20:04:39

Web Name: BioMedical Engineering OnLine | Home page

WebSite: http://biomedical-engineering-online.biomedcentral.com

ID:114932

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Engineering,BioMedical,OnLine,BioMedicalEngineeringOnLine,BiomedicalEngineeringa

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As a result of the significant disruption that is being caused by the COVID-19 pandemic we are very aware that many researchers will have difficulty in meeting the timelines associated with our peer review process during normal times. Please do let us know if you need additional time. Our systems will continue to remind you of the original timelines but we intend to be highly flexible at this time. Authors: Shuang-Lin Zhao, Gui Jin, Ze-Lin Bai, Jing-Bo Chen, Meng-Wei Li, Gen Li, Wei Zhuang, Yue-Ning Liu and Ming-Xin Qin Content type: Research 11 November 2020 Authors: Fariborz Rahimi, Reza Eyvazpour, Nazila Salahshour and Mahmood Reza Azghani Content type: Review 9 November 2020 Authors: Matija Milosevic, Cesar Marquez-Chin, Kei Masani, Masayuki Hirata, Taishin Nomura, Milos R. Popovic and Kimitaka Nakazawa Content type: Review 4 November 2020 Authors: Esmail Nourmohammadi, Saman Hosseinkhani, Reza Nedaeinia, Hoda Khoshdel-Sarkarizi, Mozhdeh Nedaeinia, Maryam Ranjbar, Neshat Ebrahimi, Zahra Farjami, Mohammad Nourmohammadi, Ali Mahmoudi, Mohammad Goli, Gordon A. Ferns and Majid Sadeghizadeh Content type: Research 19 October 2020 Authors: Hiram Madero Orozco, Osslan Osiris Vergara Villegas, Vianey Guadalupe Cruz S谩nchez, Humberto de Jes煤s Ochoa Dom铆nguez and Manuel de Jes煤s Nandayapa Alfaro Content type: Research 12 February 2015 2019Thematic seriesDeep learning in biomedical engineeringEdited byRobert Koprowski2018Thematic seriesAdvanced signal processing and modeling for neuroengineeringEdited byFei Chen, Shi-xiong Chen and Dong-mei HaoThematic seriesTrends in e-health and m-health: technology developments for improved healthcareEdited byAlessia PaglialongaThematic seriesArtificial intelligence in biomedical imagingEdited byRobert KoprowskiThematic seriesAdvances in medical robotics and automation for surgery and rehabilitationEdited byDhanjooGhista and KelvinWong2015Thematic seriesBiomedical engineering and the heart:coronary blood flow, myocardial perfusion, myocardial ischemia and infarcts detection, customized stents and bypass surgeryEdited by Dhanjoo Ghista and Kelvin WongThematic seriesAdvances in neuroimaging: insights into neurological and psychiatric diseaseCross-journal seriesEdited by Kelvin Wong BioMedical Engineering Online, in partnership with Research Square, is now offering In Review. Authors choosing this free optional service will be able to: Share their work with fellow researchers to read, comment on, and cite even before publication Showcase their work to funders and others with a citable DOI while it is still under review Track their manuscript - including seeing when reviewers are invited, and when reports are received Ervin Sejdic, University of Pittsburgh, USAFong-Chin Su, National Cheng Kung University, Taiwan BioMedical Engineering OnLine is an open access, peer-reviewed journal that is dedicated to publishing research in all areas of biomedical engineering.BioMedical Engineering OnLine is aimed at readers and authors throughout the world with an interest in using tools of the physical and data sciences, and techniques in engineering, to understand and solve problems in the biological and medical sciences. Read more. A framework for glycemic control of critically ill patients, that is personalized and risk-based, can be safely performed with a lower measurement frequency, reducing the intensive care workload. Deep Learning in medicine is one of the most rapidly and new developing fields of science. Currently, almost every device intended for medical imaging has a more or less extended image and signal analysis and processing module which can use deep learning. It provides quantitative data necessary to make a diagnosis. The obtained quantitative features must be independent of the inter-subject variability and the type of medical device and, above all, must allow for reproducible results in the presence of high noise. The proposed deep learning algorithms should also ensure the independence of the results obtained by the operator of the imaging device and, to be more exact, its position relative to the patient or the parameter settings in the device. In addition, the proposed deep learning algorithms must be tailored for the diagnosis of a specific disease entity. On the other hand, they must allow for reproducible results for high inter-subject variability. These criteria make it difficult to propose a methodology for the deep learning algorithms. This special issue is dedicated to this area of knowledge.Read the collection Our journal鈥檚 policy is for all data, previously unreported software or custom code described in the manuscript to be made freely available for any scientist wishing to use them for non-commercial purposes, without breaching participant confidentiality. All authors must include an 鈥楢vailability of Data and Materials鈥?section in their manuscript, detailing where the data, software or code supporting their findings can be found. Authors who do not wish to share their data and materials must state that it will not be shared, and give the reason. However, these authors are still expected to make their data and materials available to reviewers on a confidential basis. Please see BioMed Central鈥檚 Editorial policies page for more information. Speed77daysto first decision for reviewed manuscripts only40 days to first decision for all manuscripts171days from submission to acceptance14days from acceptance to publicationCitation Impact2.059 -2-year Impact Factor2.182 -5-year Impact Factor1.167 -Source Normalized Impactper Paper (SNIP)0.578 - SCImago Journal Rank (SJR)Usage737,046 downloads63 Altmetricmentions

TAGS:Engineering BioMedical OnLine BioMedicalEngineeringOnLine BiomedicalEngineeringa

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