Inference-based subject atypicality and signal quality indicators for physiological data

Ali Tivay; George C. Kramer; Jin Oh Hahn

doi:10.1145/3446913.3460316

Inference-based subject atypicality and signal quality indicators for physiological data

Ali Tivay, George C. Kramer, Jin Oh Hahn

Anesthesiology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

Physiological measurements are an integral part of many established and emerging engineering and biomedical applications that involve physiological modeling, physiological state estimation, and physiological closed loop control. In practice, such measurements exhibit a large degree of variability, which is apparent at multiple levels, including disturbances acting on measured signals and unexpected physiological behavior in certain individuals. In this short paper, we present an inference-based approach to estimating the atypicality of an individual's physiological data both at the level of measurement and physiological behavior. For this purpose, we use data from a cohort of subjects to infer, simultaneously, model representations for measurement disturbances and atypicality of physiological behavior. Using a case study on hematocrit (HCT), cardiac output (CO), and mean arterial pressure (MAP) measurements in response to hemorrhage and colloid infusions, we discuss the merits of the presented approach in deriving reliable subject atypicality and signal quality indicators for physiological data.

Original language	English (US)
Title of host publication	MCPS 2021 - Proceedings of the 2021 Medical Cyber Physical Systems and Internet of Medical Things
Publisher	Association for Computing Machinery, Inc
Pages	7-11
Number of pages	5
ISBN (Electronic)	9781450383271
DOIs	https://doi.org/10.1145/3446913.3460316
State	Published - May 18 2021
Event	11th Medical Cyber Physical Systems and Internet of Things Workshop, MCPS 2021, part of CPS-IoT Week 2021 - Virtual, Online, United States Duration: May 18 2021 → …

Publication series

Name	MCPS 2021 - Proceedings of the 2021 Medical Cyber Physical Systems and Internet of Medical Things

Conference

Conference	11th Medical Cyber Physical Systems and Internet of Things Workshop, MCPS 2021, part of CPS-IoT Week 2021
Country/Territory	United States
City	Virtual, Online
Period	5/18/21 → …

Keywords

arterial pressure
cardiac output
hematocrit
hemorrhage
physiological data
probabilistic inference
resuscitation
signal quality

ASJC Scopus subject areas

Human-Computer Interaction
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition
Hardware and Architecture

Access to Document

10.1145/3446913.3460316

Cite this

Tivay, A., Kramer, G. C., & Hahn, J. O. (2021). Inference-based subject atypicality and signal quality indicators for physiological data. In MCPS 2021 - Proceedings of the 2021 Medical Cyber Physical Systems and Internet of Medical Things (pp. 7-11). (MCPS 2021 - Proceedings of the 2021 Medical Cyber Physical Systems and Internet of Medical Things). Association for Computing Machinery, Inc. https://doi.org/10.1145/3446913.3460316

Inference-based subject atypicality and signal quality indicators for physiological data. / Tivay, Ali; Kramer, George C.; Hahn, Jin Oh.
MCPS 2021 - Proceedings of the 2021 Medical Cyber Physical Systems and Internet of Medical Things. Association for Computing Machinery, Inc, 2021. p. 7-11 (MCPS 2021 - Proceedings of the 2021 Medical Cyber Physical Systems and Internet of Medical Things).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Tivay, A, Kramer, GC & Hahn, JO 2021, Inference-based subject atypicality and signal quality indicators for physiological data. in MCPS 2021 - Proceedings of the 2021 Medical Cyber Physical Systems and Internet of Medical Things. MCPS 2021 - Proceedings of the 2021 Medical Cyber Physical Systems and Internet of Medical Things, Association for Computing Machinery, Inc, pp. 7-11, 11th Medical Cyber Physical Systems and Internet of Things Workshop, MCPS 2021, part of CPS-IoT Week 2021, Virtual, Online, United States, 5/18/21. https://doi.org/10.1145/3446913.3460316

Tivay A, Kramer GC, Hahn JO. Inference-based subject atypicality and signal quality indicators for physiological data. In MCPS 2021 - Proceedings of the 2021 Medical Cyber Physical Systems and Internet of Medical Things. Association for Computing Machinery, Inc. 2021. p. 7-11. (MCPS 2021 - Proceedings of the 2021 Medical Cyber Physical Systems and Internet of Medical Things). doi: 10.1145/3446913.3460316

Tivay, Ali ; Kramer, George C. ; Hahn, Jin Oh. / Inference-based subject atypicality and signal quality indicators for physiological data. MCPS 2021 - Proceedings of the 2021 Medical Cyber Physical Systems and Internet of Medical Things. Association for Computing Machinery, Inc, 2021. pp. 7-11 (MCPS 2021 - Proceedings of the 2021 Medical Cyber Physical Systems and Internet of Medical Things).

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abstract = "Physiological measurements are an integral part of many established and emerging engineering and biomedical applications that involve physiological modeling, physiological state estimation, and physiological closed loop control. In practice, such measurements exhibit a large degree of variability, which is apparent at multiple levels, including disturbances acting on measured signals and unexpected physiological behavior in certain individuals. In this short paper, we present an inference-based approach to estimating the atypicality of an individual's physiological data both at the level of measurement and physiological behavior. For this purpose, we use data from a cohort of subjects to infer, simultaneously, model representations for measurement disturbances and atypicality of physiological behavior. Using a case study on hematocrit (HCT), cardiac output (CO), and mean arterial pressure (MAP) measurements in response to hemorrhage and colloid infusions, we discuss the merits of the presented approach in deriving reliable subject atypicality and signal quality indicators for physiological data.",

keywords = "arterial pressure, cardiac output, hematocrit, hemorrhage, physiological data, probabilistic inference, resuscitation, signal quality",

author = "Ali Tivay and Kramer, {George C.} and Hahn, {Jin Oh}",

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N2 - Physiological measurements are an integral part of many established and emerging engineering and biomedical applications that involve physiological modeling, physiological state estimation, and physiological closed loop control. In practice, such measurements exhibit a large degree of variability, which is apparent at multiple levels, including disturbances acting on measured signals and unexpected physiological behavior in certain individuals. In this short paper, we present an inference-based approach to estimating the atypicality of an individual's physiological data both at the level of measurement and physiological behavior. For this purpose, we use data from a cohort of subjects to infer, simultaneously, model representations for measurement disturbances and atypicality of physiological behavior. Using a case study on hematocrit (HCT), cardiac output (CO), and mean arterial pressure (MAP) measurements in response to hemorrhage and colloid infusions, we discuss the merits of the presented approach in deriving reliable subject atypicality and signal quality indicators for physiological data.

AB - Physiological measurements are an integral part of many established and emerging engineering and biomedical applications that involve physiological modeling, physiological state estimation, and physiological closed loop control. In practice, such measurements exhibit a large degree of variability, which is apparent at multiple levels, including disturbances acting on measured signals and unexpected physiological behavior in certain individuals. In this short paper, we present an inference-based approach to estimating the atypicality of an individual's physiological data both at the level of measurement and physiological behavior. For this purpose, we use data from a cohort of subjects to infer, simultaneously, model representations for measurement disturbances and atypicality of physiological behavior. Using a case study on hematocrit (HCT), cardiac output (CO), and mean arterial pressure (MAP) measurements in response to hemorrhage and colloid infusions, we discuss the merits of the presented approach in deriving reliable subject atypicality and signal quality indicators for physiological data.

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Inference-based subject atypicality and signal quality indicators for physiological data

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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