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. 2023 Apr 11;23(8):3890.
doi: 10.3390/s23083890.

An Open-Source, Interoperable Architecture for Generating Real-Time Surgical Team Cognitive Alerts from Heart-Rate Variability Monitoring

David Arney  1   2 , Yi Zhang  1 , Lauren R Kennedy-Metz  3 , Roger D Dias  4 , Julian M Goldman  1   2 , Marco A Zenati  5
Affiliations

An Open-Source, Interoperable Architecture for Generating Real-Time Surgical Team Cognitive Alerts from Heart-Rate Variability Monitoring

David Arney et al. Sensors (Basel). .

Abstract

Clinical alarm and decision support systems that lack clinical context may create non-actionable nuisance alarms that are not clinically relevant and can cause distractions during the most difficult moments of a surgery. We present a novel, interoperable, real-time system for adding contextual awareness to clinical systems by monitoring the heart-rate variability (HRV) of clinical team members. We designed an architecture for real-time capture, analysis, and presentation of HRV data from multiple clinicians and implemented this architecture as an application and device interfaces on the open-source OpenICE interoperability platform. In this work, we extend OpenICE with new capabilities to support the needs of the context-aware OR including a modularized data pipeline for simultaneously processing real-time electrocardiographic (ECG) waveforms from multiple clinicians to create estimates of their individual cognitive load. The system is built with standardized interfaces that allow for free interchange of software and hardware components including sensor devices, ECG filtering and beat detection algorithms, HRV metric calculations, and individual and team alerts based on changes in metrics. By integrating contextual cues and team member state into a unified process model, we believe future clinical applications will be able to emulate some of these behaviors to provide context-aware information to improve the safety and quality of surgical interventions.

Keywords: cognitive load; heart-rate variability; interoperability; smart alarms; surgical data science.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
COR-ICE Architecture.
Figure 2
Figure 2
Stages of the Processing Pipeline.
Figure 3
Figure 3
Application Design Wireframe.
Figure 4
Figure 4
COR-ICE HRV Analysis App UI.
See this image and copyright information in PMC

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