A Physiological-Model-Based Neural Network Framework for Blood Pressure Estimation from Photoplethysmography Signals

Yaowen Zhang; Libera Fresiello; Peter H. Veltink; Dirk W. Donker; Ying Wang

doi:10.1109/EMBC58623.2025.11251595

A Physiological-Model-Based Neural Network Framework for Blood Pressure Estimation from Photoplethysmography Signals

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

Abstract

Continuous blood pressure (BP) estimation via photoplethysmography (PPG) remains a significant challenge, particularly in providing comprehensive cardiovascular insights for hypertensive complications. This study presents a novel physiological model-based neural network (PMB-NN) framework for BP estimation from PPG signals, incorporating the identification of total peripheral resistance (TPR) and arterial compliance (AC) to enhance physiological interpretability. Preliminary experimental results, obtained from a single healthy participant under varying activity intensities, demonstrated promising accuracy, with a median standard deviation of 6.88 mmHg for systolic BP and 3.72 mmHg for diastolic BP. The median error for TPR and AC was 0.048 mmHg·s/ml and -0.521 ml/mmHg, respectively. Consistent with expectations, both estimated TPR and AC exhibited a reduction as activity intensity increased.

Original language	English
Title of host publication	2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Publisher	IEEE
Number of pages	5
ISBN (Electronic)	979-8-3315-8618-8
DOIs	https://doi.org/10.1109/EMBC58623.2025.11251595
Publication status	Published - 3 Dec 2025
Event	47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2025 - Bella Center, Copenhagen, Denmark Duration: 14 Jul 2025 → 17 Jul 2025 Conference number: 47 https://embc.embs.org/2025/ https://embc.embs.org/2025/about/

Conference

Conference	47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2025
Abbreviated title	EMBC 2025
Country/Territory	Denmark
City	Copenhagen
Period	14/07/25 → 17/07/25
Internet address	https://embc.embs.org/2025/ https://embc.embs.org/2025/about/

Keywords

2026 OA procedure

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10.1109/EMBC58623.2025.11251595

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@inproceedings{8583337088b6451480540d57d4981cea,

title = "A Physiological-Model-Based Neural Network Framework for Blood Pressure Estimation from Photoplethysmography Signals",

abstract = "Continuous blood pressure (BP) estimation via photoplethysmography (PPG) remains a significant challenge, particularly in providing comprehensive cardiovascular insights for hypertensive complications. This study presents a novel physiological model-based neural network (PMB-NN) framework for BP estimation from PPG signals, incorporating the identification of total peripheral resistance (TPR) and arterial compliance (AC) to enhance physiological interpretability. Preliminary experimental results, obtained from a single healthy participant under varying activity intensities, demonstrated promising accuracy, with a median standard deviation of 6.88 mmHg for systolic BP and 3.72 mmHg for diastolic BP. The median error for TPR and AC was 0.048 mmHg·s/ml and -0.521 ml/mmHg, respectively. Consistent with expectations, both estimated TPR and AC exhibited a reduction as activity intensity increased.",

keywords = "2026 OA procedure",

author = "Yaowen Zhang and Libera Fresiello and Veltink, \{Peter H.\} and Donker, \{Dirk W.\} and Ying Wang",

year = "2025",

month = dec,

day = "3",

doi = "10.1109/EMBC58623.2025.11251595",

language = "English",

booktitle = "2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)",

publisher = "IEEE",

address = "United States",

note = "47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2025, EMBC 2025 ; Conference date: 14-07-2025 Through 17-07-2025",

url = "https://embc.embs.org/2025/, https://embc.embs.org/2025/about/",

}

Zhang, Y, Fresiello, L , Veltink, PH , Donker, DW & Wang, Y 2025, A Physiological-Model-Based Neural Network Framework for Blood Pressure Estimation from Photoplethysmography Signals. in 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2025, Copenhagen, Denmark, 14/07/25. https://doi.org/10.1109/EMBC58623.2025.11251595

A Physiological-Model-Based Neural Network Framework for Blood Pressure Estimation from Photoplethysmography Signals. / Zhang, Yaowen; Fresiello, Libera ; Veltink, Peter H. et al.
2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2025.

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

TY - GEN

T1 - A Physiological-Model-Based Neural Network Framework for Blood Pressure Estimation from Photoplethysmography Signals

AU - Zhang, Yaowen

AU - Fresiello, Libera

AU - Veltink, Peter H.

AU - Donker, Dirk W.

AU - Wang, Ying

N1 - Conference code: 47

PY - 2025/12/3

Y1 - 2025/12/3

N2 - Continuous blood pressure (BP) estimation via photoplethysmography (PPG) remains a significant challenge, particularly in providing comprehensive cardiovascular insights for hypertensive complications. This study presents a novel physiological model-based neural network (PMB-NN) framework for BP estimation from PPG signals, incorporating the identification of total peripheral resistance (TPR) and arterial compliance (AC) to enhance physiological interpretability. Preliminary experimental results, obtained from a single healthy participant under varying activity intensities, demonstrated promising accuracy, with a median standard deviation of 6.88 mmHg for systolic BP and 3.72 mmHg for diastolic BP. The median error for TPR and AC was 0.048 mmHg·s/ml and -0.521 ml/mmHg, respectively. Consistent with expectations, both estimated TPR and AC exhibited a reduction as activity intensity increased.

AB - Continuous blood pressure (BP) estimation via photoplethysmography (PPG) remains a significant challenge, particularly in providing comprehensive cardiovascular insights for hypertensive complications. This study presents a novel physiological model-based neural network (PMB-NN) framework for BP estimation from PPG signals, incorporating the identification of total peripheral resistance (TPR) and arterial compliance (AC) to enhance physiological interpretability. Preliminary experimental results, obtained from a single healthy participant under varying activity intensities, demonstrated promising accuracy, with a median standard deviation of 6.88 mmHg for systolic BP and 3.72 mmHg for diastolic BP. The median error for TPR and AC was 0.048 mmHg·s/ml and -0.521 ml/mmHg, respectively. Consistent with expectations, both estimated TPR and AC exhibited a reduction as activity intensity increased.

KW - 2026 OA procedure

UR - https://www.scopus.com/pages/publications/105023715507

U2 - 10.1109/EMBC58623.2025.11251595

DO - 10.1109/EMBC58623.2025.11251595

M3 - Conference contribution

C2 - 41336826

AN - SCOPUS:105023715507

BT - 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

PB - IEEE

T2 - 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2025

Y2 - 14 July 2025 through 17 July 2025

ER -

A Physiological-Model-Based Neural Network Framework for Blood Pressure Estimation from Photoplethysmography Signals

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A Physiological-Model-Based Neural Network Framework for Blood Pressure Estimation from Photoplethysmography Signals

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