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Feature extraction tool using temporal landmarks in arterial blood pressure and photoplethysmography waveforms

Feature extraction tool using temporal landmarks in arterial blood pressure and photoplethysmography waveforms
Feature extraction tool using temporal landmarks in arterial blood pressure and photoplethysmography waveforms
Arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms both contain vital physiological information for the prevention and treatment of cardiovascular diseases. Extracted features from these waveforms have diverse clinical applications, including predicting hyper- and hypo-tension, estimating cardiac output from ABP, and monitoring blood pressure and nociception from PPG. However, the lack of standardized tools for feature extraction limits their exploration and clinical utilization. In this study, we propose an automatic feature extraction tool that first detects temporal location of landmarks within each cardiac cycle of ABP and PPG waveforms, including the systolic phase onset, systolic phase peak, dicrotic notch, and diastolic phase peak using the iterative envelope mean method. Then, based on these landmarks, extracts 852 features per cardiac cycle, encompassing time-, statistical-, and frequency-domains. The tool's ability to detect landmarks was evaluated using ABP and PPG waveforms from a large perioperative dataset (MLORD dataset) comprising 17,327 patients. We analyzed 34,267 cardiac cycles of ABP waveforms and 33,792 cardiac cycles of PPG waveforms. Additionally, to assess the tool's real-time landmark detection capability, we retrospectively analyzed 3,000 cardiac cycles of both ABP and PPG waveforms, collected from a Philips IntelliVue MX800 patient monitor. The tool's detection performance was assessed against markings by an experienced researcher, achieving average F1-scores and error rates for ABP and PPG as follows: (1) On MLORD dataset: systolic phase onset (99.77 %, 0.35 % and 99.52 %, 0.75 %), systolic phase peak (99.80 %, 0.30 % and 99.56 %, 0.70 %), dicrotic notch (98.24 %, 2.63 % and 98.72 %, 1.96 %), and diastolic phase peak (98.59 %, 2.11 % and 98.88 %, 1.73 %); (2) On real time data: systolic phase onset (98.18 %, 3.03 % and 97.94 %, 3.43 %), systolic phase peak (98.22 %, 2.97 % and 97.74 %, 3.77 %), dicrotic notch (97.72 %, 3.80 % and 98.16 %, 3.07 % ), and diastolic phase peak (98.04 %, 3.27 % and 98.08 %, 3.20 %). This tool has significant potential for supporting clinical utilization of ABP and PPG waveform features and for facilitating feature-based machine learning models for various clinical applications where features derived from these waveforms play a critical role.
feature exrtaction, Arterial Blood Pressure (ABP) waveforms, photoplethysmography (PPG) waveforms
2948-2836
Pal, Ravi
a4973d64-eac7-47db-ad19-e79e6c64abd0
Rudas, Akos
b240d5e0-dc15-4e6f-a269-6b98ee2a549e
Williams, Tiffany
05adcd88-73b5-4d7b-abf1-a2da573994ef
Chiang, Jeffrey N.
6bf2c819-4d49-4876-9647-c8c46bec0cc4
Barney, Anna
bc0ee7f7-517a-4154-ab7d-57270de3e815
Cannesson, Maxime
87706f97-7392-41ab-b0c3-96355c518efe
Pal, Ravi
a4973d64-eac7-47db-ad19-e79e6c64abd0
Rudas, Akos
b240d5e0-dc15-4e6f-a269-6b98ee2a549e
Williams, Tiffany
05adcd88-73b5-4d7b-abf1-a2da573994ef
Chiang, Jeffrey N.
6bf2c819-4d49-4876-9647-c8c46bec0cc4
Barney, Anna
bc0ee7f7-517a-4154-ab7d-57270de3e815
Cannesson, Maxime
87706f97-7392-41ab-b0c3-96355c518efe

Pal, Ravi, Rudas, Akos, Williams, Tiffany, Chiang, Jeffrey N., Barney, Anna and Cannesson, Maxime (2025) Feature extraction tool using temporal landmarks in arterial blood pressure and photoplethysmography waveforms. NPJ Cardiovascular Health, 2, [57]. (doi:10.1038/s44325-025-00096-0).

Record type: Article

Abstract

Arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms both contain vital physiological information for the prevention and treatment of cardiovascular diseases. Extracted features from these waveforms have diverse clinical applications, including predicting hyper- and hypo-tension, estimating cardiac output from ABP, and monitoring blood pressure and nociception from PPG. However, the lack of standardized tools for feature extraction limits their exploration and clinical utilization. In this study, we propose an automatic feature extraction tool that first detects temporal location of landmarks within each cardiac cycle of ABP and PPG waveforms, including the systolic phase onset, systolic phase peak, dicrotic notch, and diastolic phase peak using the iterative envelope mean method. Then, based on these landmarks, extracts 852 features per cardiac cycle, encompassing time-, statistical-, and frequency-domains. The tool's ability to detect landmarks was evaluated using ABP and PPG waveforms from a large perioperative dataset (MLORD dataset) comprising 17,327 patients. We analyzed 34,267 cardiac cycles of ABP waveforms and 33,792 cardiac cycles of PPG waveforms. Additionally, to assess the tool's real-time landmark detection capability, we retrospectively analyzed 3,000 cardiac cycles of both ABP and PPG waveforms, collected from a Philips IntelliVue MX800 patient monitor. The tool's detection performance was assessed against markings by an experienced researcher, achieving average F1-scores and error rates for ABP and PPG as follows: (1) On MLORD dataset: systolic phase onset (99.77 %, 0.35 % and 99.52 %, 0.75 %), systolic phase peak (99.80 %, 0.30 % and 99.56 %, 0.70 %), dicrotic notch (98.24 %, 2.63 % and 98.72 %, 1.96 %), and diastolic phase peak (98.59 %, 2.11 % and 98.88 %, 1.73 %); (2) On real time data: systolic phase onset (98.18 %, 3.03 % and 97.94 %, 3.43 %), systolic phase peak (98.22 %, 2.97 % and 97.74 %, 3.77 %), dicrotic notch (97.72 %, 3.80 % and 98.16 %, 3.07 % ), and diastolic phase peak (98.04 %, 3.27 % and 98.08 %, 3.20 %). This tool has significant potential for supporting clinical utilization of ABP and PPG waveform features and for facilitating feature-based machine learning models for various clinical applications where features derived from these waveforms play a critical role.

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More information

Accepted/In Press date: 28 October 2025
e-pub ahead of print date: 24 November 2025
Published date: 24 November 2025
Keywords: feature exrtaction, Arterial Blood Pressure (ABP) waveforms, photoplethysmography (PPG) waveforms

Identifiers

Local EPrints ID: 507115
URI: http://eprints.soton.ac.uk/id/eprint/507115
ISSN: 2948-2836
PURE UUID: decbbac1-136a-436a-ad1f-375a0fae88b4
ORCID for Anna Barney: ORCID iD orcid.org/0000-0002-6034-1478

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Date deposited: 27 Nov 2025 17:36
Last modified: 28 Nov 2025 02:35

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Contributors

Author: Ravi Pal
Author: Akos Rudas
Author: Tiffany Williams
Author: Jeffrey N. Chiang
Author: Anna Barney ORCID iD
Author: Maxime Cannesson

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