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      Sensors for Continuous Monitoring of Surgeon’s Cognitive Workload in the Cardiac Operating Room

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          Abstract

          Monitoring healthcare providers’ cognitive workload during surgical procedures can provide insight into the dynamic changes of mental states that may affect patient clinical outcomes. The role of cognitive factors influencing both technical and non-technical skill are increasingly being recognized, especially as the opportunities to unobtrusively collect accurate and sensitive data are improving. Applying sensors to capture these data in a complex real-world setting such as the cardiac surgery operating room, however, is accompanied by myriad social, physical, and procedural constraints. The goal of this study was to investigate the feasibility of overcoming logistical barriers in order to effectively collect multi-modal psychophysiological inputs via heart rate (HR) and near-infrared spectroscopy (NIRS) acquisition in the real-world setting of the operating room. The surgeon was outfitted with HR and NIRS sensors during aortic valve surgery, and validation analysis was performed to detect the influence of intra-operative events on cardiovascular and prefrontal cortex changes. Signals collected were significantly correlated and noted intra-operative events and subjective self-reports coincided with observable correlations among cardiovascular and cerebral activity across surgical phases. The primary novelty and contribution of this work is in demonstrating the feasibility of collecting continuous sensor data from a surgical team member in a real-world setting.

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          Most cited references33

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          Heart rate variability: Standards of measurement, physiological interpretation, and clinical use

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            Kubios HRV--heart rate variability analysis software.

            Kubios HRV is an advanced and easy to use software for heart rate variability (HRV) analysis. The software supports several input data formats for electrocardiogram (ECG) data and beat-to-beat RR interval data. It includes an adaptive QRS detection algorithm and tools for artifact correction, trend removal and analysis sample selection. The software computes all the commonly used time-domain and frequency-domain HRV parameters and several nonlinear parameters. There are several adjustable analysis settings through which the analysis methods can be optimized for different data. The ECG derived respiratory frequency is also computed, which is important for reliable interpretation of the analysis results. The analysis results can be saved as an ASCII text file (easy to import into MS Excel or SPSS), Matlab MAT-file, or as a PDF report. The software is easy to use through its compact graphical user interface. The software is available free of charge for Windows and Linux operating systems at http://kubios.uef.fi. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
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              A meta-analysis of heart rate variability and neuroimaging studies: implications for heart rate variability as a marker of stress and health.

              The intimate connection between the brain and the heart was enunciated by Claude Bernard over 150 years ago. In our neurovisceral integration model we have tried to build on this pioneering work. In the present paper we further elaborate our model and update it with recent results. Specifically, we performed a meta-analysis of recent neuroimaging studies on the relationship between heart rate variability and regional cerebral blood flow. We identified a number of regions, including the amygdala and ventromedial prefrontal cortex, in which significant associations across studies were found. We further propose that the default response to uncertainty is the threat response and may be related to the well known negativity bias. Heart rate variability may provide an index of how strongly 'top-down' appraisals, mediated by cortical-subcortical pathways, shape brainstem activity and autonomic responses in the body. If the default response to uncertainty is the threat response, as we propose here, contextual information represented in 'appraisal' systems may be necessary to overcome this bias during daily life. Thus, HRV may serve as a proxy for 'vertical integration' of the brain mechanisms that guide flexible control over behavior with peripheral physiology, and as such provides an important window into understanding stress and health. Copyright © 2011 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                19 November 2020
                November 2020
                : 20
                : 22
                : 6616
                Affiliations
                [1 ]Division of Cardiac Surgery, Medical Robotics and Computer Assisted Surgery Lab, VA Boston Healthcare System, West Roxbury, MA 02132, USA; marco_zenati@ 123456hms.harvard.edu
                [2 ]Department of Surgery, Harvard Medical School, Boston, MA 02115, USA
                [3 ]STRATUS Center for Medical Simulation, Department of Emergency Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA; rdias@ 123456bwh.harvard.edu
                [4 ]Division of Cardiac Surgery, VA Boston Healthcare System, West Roxbury, MA 02132, USA; rithy.srey@ 123456va.gov (R.S.); geoffrey.rance@ 123456va.gov (G.C.R.)
                [5 ]HK3 Lab, 20129 Milan, Italy; cesare.furlanello@ 123456hk3lab.ai
                Author notes
                [* ]Correspondence: lauren.kennedy-metz@ 123456va.gov ; Tel.: +1-857-203-6180
                Author information
                https://orcid.org/0000-0002-2696-3943
                https://orcid.org/0000-0003-4959-5052
                https://orcid.org/0000-0001-7139-0323
                Article
                sensors-20-06616
                10.3390/s20226616
                7699221
                33227967
                fd41c44c-5f09-4a2c-8d39-b9b40a999c6f
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 15 October 2020
                : 16 November 2020
                Categories
                Letter

                Biomedical engineering
                cognitive workload,cardiac surgery,heart rate,near-infrared spectroscopy

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