Literature DB >> 17056954

The problem of artifacts in patient monitor data during surgery: a clinical and methodological review.

George Takla1, John H Petre, D John Doyle, Mayumi Horibe, Bala Gopakumaran.   

Abstract

Artifacts are a significant problem affecting the accurate display of information during surgery. They are also a source of false alarms. A secondary problem is the inadvertent recording of artifactual and inaccurate information in automated record keeping systems. Though most of the currently available patient monitors use techniques to minimize the effect of artifacts, their success is limited. We reviewed the problem of artifacts affecting patient monitor data during surgical cases. Methods adopted by currently marketed patient monitors to eliminate and minimize artifacts due to technical and environmental factors are reviewed and discussed. Also discussed are promising artifact detection and correction methods that are being investigated. These might be used to detect and eliminate artifacts with improved accuracy and specificity.

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Year:  2006        PMID: 17056954     DOI: 10.1213/01.ane.0000247964.47706.5d

Source DB:  PubMed          Journal:  Anesth Analg        ISSN: 0003-2999            Impact factor:   5.108


  16 in total

1.  Reduction of clinically irrelevant alarms in patient monitoring by adaptive time delays.

Authors:  Felix Schmid; Matthias S Goepfert; Frank Franz; David Laule; Beate Reiter; Alwin E Goetz; Daniel A Reuter
Journal:  J Clin Monit Comput       Date:  2015-11-30       Impact factor: 2.502

Review 2.  Using the features of the time and volumetric capnogram for classification and prediction.

Authors:  Michael B Jaffe
Journal:  J Clin Monit Comput       Date:  2016-01-18       Impact factor: 2.502

3.  Sensor fusion using a hybrid median filter for artifact removal in intraoperative heart rate monitoring.

Authors:  Ping Yang; Guy A Dumont; J Mark Ansermino
Journal:  J Clin Monit Comput       Date:  2009-02-07       Impact factor: 2.502

4.  A knowledge authoring tool for clinical decision support.

Authors:  Dustin Dunsmuir; Jeremy Daniels; Christopher Brouse; Simon Ford; J Mark Ansermino
Journal:  J Clin Monit Comput       Date:  2008-05-08       Impact factor: 2.502

5.  Automated anesthesia artifact analysis: can machines be trained to take out the garbage?

Authors:  Allan F Simpao; Olivia Nelson; Luis M Ahumada
Journal:  J Clin Monit Comput       Date:  2020-09-12       Impact factor: 2.502

6.  Real alerts and artifact classification in archived multi-signal vital sign monitoring data: implications for mining big data.

Authors:  Marilyn Hravnak; Lujie Chen; Artur Dubrawski; Eliezer Bose; Gilles Clermont; Michael R Pinsky
Journal:  J Clin Monit Comput       Date:  2015-10-05       Impact factor: 2.502

7.  Development and Feasibility of a Real-Time Clinical Decision Support System for Traumatic Brain Injury Anesthesia Care.

Authors:  Taniga Kiatchai; Ashley A Colletti; Vivian H Lyons; Rosemary M Grant; Monica S Vavilala; Bala G Nair
Journal:  Appl Clin Inform       Date:  2017-01-25       Impact factor: 2.342

8.  Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multisignal Vital Sign Monitoring Data.

Authors:  Lujie Chen; Artur Dubrawski; Donghan Wang; Madalina Fiterau; Mathieu Guillame-Bert; Eliezer Bose; Ata M Kaynar; David J Wallace; Jane Guttendorf; Gilles Clermont; Michael R Pinsky; Marilyn Hravnak
Journal:  Crit Care Med       Date:  2016-07       Impact factor: 7.598

9.  An Unusual Cause of Electrocardiographic Artefact: A Patient's Warming Blanket.

Authors:  Gaurav Misra; Sanjay Dhiraaj; Aditya Kapoor; Puneet Goyal
Journal:  Turk J Anaesthesiol Reanim       Date:  2018-04-01

10.  Robust parameter extraction for decision support using multimodal intensive care data.

Authors:  G D Clifford; W J Long; G B Moody; P Szolovits
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2009-01-28       Impact factor: 4.226

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