From peripheral finger-derived pulse waveforms to aortic pressure waveform features: an application of a generalized transfer function

James R. Cox; Isabella Tan; Ahmad Qasem; Alberto P. Avolio; Mark Butlin

doi:10.1109/EMBC40787.2023.10340434

From peripheral finger-derived pulse waveforms to aortic pressure waveform features: an application of a generalized transfer function

James R. Cox, Isabella Tan, Ahmad Qasem, Alberto P. Avolio, Mark Butlin

Macquarie Medical School

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

1 Citation (Scopus)

Abstract

Objective: Aortic (central) pressure features are associated with cardiovascular complications and can be algorithmically derived from non-invasive peripheral arterial waveforms. This has conventionally been performed with a pressure waveform (i.e., tonometry or oscillometry) rather than with the optical-based sensor (photoplethysmography (PPG)) that is predominantly used in wearable health devices. Extraction of aortic features from a peripheral PPG waveform has yet to be investigated. This study aims to compare aortic features extracted from peripheral arterial waveforms acquired with different sensor modalities using the same transfer function.

Design and Method: Radial tonometry (reference), finger volume-clamped PPG (Peňáz) and fingertip PPG waveforms were measured in participants (n=29, 36±16 years, 15 female) under baseline conditions. Waveforms were converted into an aortic pressure waveform using the transfer function. Waveform features were extracted from the converted waveform. Extracted features were compared with correlation plots and a Bland-Altman analysis.

Results: Aortic pressure features extracted from a finger using the Peňáz technique were comparable to radial tonometry derived features. Aortic features extracted from a fingertip waveform were more variable in comparison to radial tonometry-derived features.

Conclusions: Aortic (central) pressure waveform features contain valuable haemodynamic information and have the capacity to be easily and conveniently implemented in wearable health devices. Future use of these features in wearable health devices incorporating PPG requires the development, and/or, optimization of a unique transfer function to more accurately represent the aortic pressure waveform for cardiovascular assessment. Clinical Relevance- Aortic pressure features might be used in wearable health devices following the development of a unique transfer function for optical-transduced peripheral vascular signals.

Original language	English
Title of host publication	2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC)
Place of Publication	USA
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Number of pages	4
ISBN (Electronic)	9798350324471
ISBN (Print)	9798350324488
DOIs	https://doi.org/10.1109/EMBC40787.2023.10340434
Publication status	Published - 1 Jul 2023
Event	Annual International Conference of the IEEE Engineering in Medicine and Biology Conference (45th : 2023) - Sydney, Australia Duration: 24 Jul 2023 → 27 Jul 2023

Publication series

Name	Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Volume	2023
ISSN (Electronic)	2694-0604

Conference

Conference	Annual International Conference of the IEEE Engineering in Medicine and Biology Conference (45th : 2023)
Abbreviated title	EMBC 2023
Country/Territory	Australia
City	Sydney
Period	24/07/23 → 27/07/23

Keywords

Humans
Female
Arterial Pressure
Blood Pressure
Arteries
Hemodynamics
Aorta

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10.1109/EMBC40787.2023.10340434

Cite this

Cox, J. R., Tan, I., Qasem, A., Avolio, A. P., & Butlin, M. (2023). From peripheral finger-derived pulse waveforms to aortic pressure waveform features: an application of a generalized transfer function. In 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC) (Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Vol. 2023). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/EMBC40787.2023.10340434

Cox, James R. ; Tan, Isabella ; Qasem, Ahmad et al. / From peripheral finger-derived pulse waveforms to aortic pressure waveform features : an application of a generalized transfer function. 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC). USA : Institute of Electrical and Electronics Engineers (IEEE), 2023. (Annual International Conference of the IEEE Engineering in Medicine and Biology Society).

@inproceedings{49f08ff4e41b4e00aebeacd0f4b5bd3c,

title = "From peripheral finger-derived pulse waveforms to aortic pressure waveform features: an application of a generalized transfer function",

abstract = "Objective: Aortic (central) pressure features are associated with cardiovascular complications and can be algorithmically derived from non-invasive peripheral arterial waveforms. This has conventionally been performed with a pressure waveform (i.e., tonometry or oscillometry) rather than with the optical-based sensor (photoplethysmography (PPG)) that is predominantly used in wearable health devices. Extraction of aortic features from a peripheral PPG waveform has yet to be investigated. This study aims to compare aortic features extracted from peripheral arterial waveforms acquired with different sensor modalities using the same transfer function.Design and Method: Radial tonometry (reference), finger volume-clamped PPG (Pe{\v n}{\'a}z) and fingertip PPG waveforms were measured in participants (n=29, 36±16 years, 15 female) under baseline conditions. Waveforms were converted into an aortic pressure waveform using the transfer function. Waveform features were extracted from the converted waveform. Extracted features were compared with correlation plots and a Bland-Altman analysis.Results: Aortic pressure features extracted from a finger using the Pe{\v n}{\'a}z technique were comparable to radial tonometry derived features. Aortic features extracted from a fingertip waveform were more variable in comparison to radial tonometry-derived features.Conclusions: Aortic (central) pressure waveform features contain valuable haemodynamic information and have the capacity to be easily and conveniently implemented in wearable health devices. Future use of these features in wearable health devices incorporating PPG requires the development, and/or, optimization of a unique transfer function to more accurately represent the aortic pressure waveform for cardiovascular assessment. Clinical Relevance- Aortic pressure features might be used in wearable health devices following the development of a unique transfer function for optical-transduced peripheral vascular signals.",

keywords = "Humans, Female, Arterial Pressure, Blood Pressure, Arteries, Hemodynamics, Aorta",

author = "Cox, {James R.} and Isabella Tan and Ahmad Qasem and Avolio, {Alberto P.} and Mark Butlin",

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publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

booktitle = "2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC)",

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Cox, JR, Tan, I, Qasem, A, Avolio, AP & Butlin, M 2023, From peripheral finger-derived pulse waveforms to aortic pressure waveform features: an application of a generalized transfer function. in 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC). Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 2023, Institute of Electrical and Electronics Engineers (IEEE), USA, Annual International Conference of the IEEE Engineering in Medicine and Biology Conference (45th : 2023), Sydney, New South Wales, Australia, 24/07/23. https://doi.org/10.1109/EMBC40787.2023.10340434

From peripheral finger-derived pulse waveforms to aortic pressure waveform features: an application of a generalized transfer function. / Cox, James R.; Tan, Isabella; Qasem, Ahmad et al.
2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC). USA: Institute of Electrical and Electronics Engineers (IEEE), 2023. (Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Vol. 2023).

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

TY - GEN

T1 - From peripheral finger-derived pulse waveforms to aortic pressure waveform features

T2 - Annual International Conference of the IEEE Engineering in Medicine and Biology Conference (45th : 2023)

AU - Cox, James R.

AU - Tan, Isabella

AU - Qasem, Ahmad

AU - Avolio, Alberto P.

AU - Butlin, Mark

PY - 2023/7/1

Y1 - 2023/7/1

N2 - Objective: Aortic (central) pressure features are associated with cardiovascular complications and can be algorithmically derived from non-invasive peripheral arterial waveforms. This has conventionally been performed with a pressure waveform (i.e., tonometry or oscillometry) rather than with the optical-based sensor (photoplethysmography (PPG)) that is predominantly used in wearable health devices. Extraction of aortic features from a peripheral PPG waveform has yet to be investigated. This study aims to compare aortic features extracted from peripheral arterial waveforms acquired with different sensor modalities using the same transfer function.Design and Method: Radial tonometry (reference), finger volume-clamped PPG (Peňáz) and fingertip PPG waveforms were measured in participants (n=29, 36±16 years, 15 female) under baseline conditions. Waveforms were converted into an aortic pressure waveform using the transfer function. Waveform features were extracted from the converted waveform. Extracted features were compared with correlation plots and a Bland-Altman analysis.Results: Aortic pressure features extracted from a finger using the Peňáz technique were comparable to radial tonometry derived features. Aortic features extracted from a fingertip waveform were more variable in comparison to radial tonometry-derived features.Conclusions: Aortic (central) pressure waveform features contain valuable haemodynamic information and have the capacity to be easily and conveniently implemented in wearable health devices. Future use of these features in wearable health devices incorporating PPG requires the development, and/or, optimization of a unique transfer function to more accurately represent the aortic pressure waveform for cardiovascular assessment. Clinical Relevance- Aortic pressure features might be used in wearable health devices following the development of a unique transfer function for optical-transduced peripheral vascular signals.

AB - Objective: Aortic (central) pressure features are associated with cardiovascular complications and can be algorithmically derived from non-invasive peripheral arterial waveforms. This has conventionally been performed with a pressure waveform (i.e., tonometry or oscillometry) rather than with the optical-based sensor (photoplethysmography (PPG)) that is predominantly used in wearable health devices. Extraction of aortic features from a peripheral PPG waveform has yet to be investigated. This study aims to compare aortic features extracted from peripheral arterial waveforms acquired with different sensor modalities using the same transfer function.Design and Method: Radial tonometry (reference), finger volume-clamped PPG (Peňáz) and fingertip PPG waveforms were measured in participants (n=29, 36±16 years, 15 female) under baseline conditions. Waveforms were converted into an aortic pressure waveform using the transfer function. Waveform features were extracted from the converted waveform. Extracted features were compared with correlation plots and a Bland-Altman analysis.Results: Aortic pressure features extracted from a finger using the Peňáz technique were comparable to radial tonometry derived features. Aortic features extracted from a fingertip waveform were more variable in comparison to radial tonometry-derived features.Conclusions: Aortic (central) pressure waveform features contain valuable haemodynamic information and have the capacity to be easily and conveniently implemented in wearable health devices. Future use of these features in wearable health devices incorporating PPG requires the development, and/or, optimization of a unique transfer function to more accurately represent the aortic pressure waveform for cardiovascular assessment. Clinical Relevance- Aortic pressure features might be used in wearable health devices following the development of a unique transfer function for optical-transduced peripheral vascular signals.

KW - Humans

KW - Female

KW - Arterial Pressure

KW - Blood Pressure

KW - Arteries

KW - Hemodynamics

KW - Aorta

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M3 - Conference proceeding contribution

C2 - 38083360

SN - 9798350324488

T3 - Annual International Conference of the IEEE Engineering in Medicine and Biology Society

BT - 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC)

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Cox JR, Tan I, Qasem A, Avolio AP , Butlin M. From peripheral finger-derived pulse waveforms to aortic pressure waveform features: an application of a generalized transfer function. In 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC). USA: Institute of Electrical and Electronics Engineers (IEEE). 2023. (Annual International Conference of the IEEE Engineering in Medicine and Biology Society). doi: 10.1109/EMBC40787.2023.10340434

From peripheral finger-derived pulse waveforms to aortic pressure waveform features: an application of a generalized transfer function

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