PPG2ABP: Translating photoplethysmogram (PPG) signals to arterial blood pressure (ABP) waveforms
Open accessAbstract
Cardiovascular diseases are one of the most severe causes of mortality, annually taking a heavy toll on lives worldwide. Continuous monitoring of blood pressure seems to be the most viable option, but this demands an invasive process, introducing several layers of complexities and reliability concerns due to non-invasive techniques not being accurate. This motivates us to develop a method to estimate the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using Photoplethysmogram (PPG) signals. We explore the advantage of deep learning, as it would free us from sticking to ideally shaped PPG signals only by making handcrafted feature computation irrelevant, which is a shortcoming of the existing approaches. Thus, we present PPG2ABP, a two-stage cascaded deep learning-based method that manages to estimate the continuous ABP waveform from the input PPG signal with a mean absolute error of 4.604 mmHg, preserving the shape, magnitude, and phase in unison. However, the more astounding success of PPG2ABP turns out to be that the computed values of Diastolic Blood Pressure (DBP), Mean Arterial Pressure (MAP), and Systolic Blood Pressure (SBP) from the estimated ABP waveform outperform the existing works under several metrics (mean absolute error of 3.449 ± 6.147 mmHg, 2.310 ± 4.437 mmHg, and 5.727 ± 9.162 mmHg, respectively), despite that PPG2ABP is not explicitly trained to do so. Notably, both for DBP and MAP, we achieve Grade A in the BHS (British Hypertension Society) Standard and satisfy the AAMI (Association for the Advancement of Medical Instrumentation) standard.
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Algorithm developmental biases stem from selecting features, adjusting training data composition, and model calibrations.
“The accuracy of heart rate measurements affects people with darker skin tones because of algorithm developmental biases that stem from selecting features and adjusting training data composition and model calibrations [23-26].”
From Photoplethysmography in Diverse Skin Tones: Evaluating Bias in Smartwatch Health Monitoring (2025) · cites this paper as [24]
The Experiments sections (3.2–3.5) describe choices of loss functions, filters, and training parameters for estimating arterial blood pressure from PPG signals. A search for skin tone, heart-rate accuracy, and demographic algorithmic bias found no passage linking feature selection, training-data composition, or calibration to less accurate heart-rate measurements in people with darker skin. (LLM, conf=0.77)
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Sorted by citations
Photoplethysmography in Diverse Skin Tones: Evaluating Bias in Smartwatch Health Monitoring
Asif, S., AlSaafeen, A., Nadar, S., Nambiar, S., Dannawi, J., Korrapati, H., & Wilkhoo, H. S. (2025) · Cureus · doi:10.7759/cureus.94074 · Cited by 9 · see citing works
- Authors
- Ibtehaz, N., Mahmud, S., Chowdhury, M. E. H., Khandakar, A., Salman Khan, M., Ayari, M. A., Tahir, A. M., & Rahman, M. S.
- Affiliations
- Purdue University West Lafayette (US)
- Qatar University (QA)
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
- University of British Columbia (CA)
- Bangladesh University of Engineering and Technology (BD)
- Corresponding author
- Chowdhury, Muhammad E. H.
- Rahman, M. Sohel
- ORCID
- Venue
- Bioengineering
- Publisher
- MDPI AG
- Published
- 2022-11-15
- Volume
- 9
- Issue
- 11
- Pages
- 692
- ISSN
- 2306-5354 (ISSN-L)
- PMC
- PMC9687508
- OpenAlex
- W4309119158
- Cited by
- 133
- References
- 71
- Citations by year
- 2026: 27 · 2025: 36 · 2024: 37 · 2023: 21 · 2022: 10 · 2021: 1 · 2020: 1
- FWCI
- 7.56
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Bioengineering (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- cIRcle (University of British Columbia) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Qatar University QSpace (Qatar University) (submittedVersion)
- Open Collections
- Funders
- Qatar National Research Fund (NPRP12S-0227-190164, QUST-2-CENG-2022-711)
- Qatar University (NPRP12S-0227-190164, QUST-2-CENG-2022-711)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Disease Prevention
- Health Sciences › Medicine › Surgery › Hemodynamic Monitoring and Therapy
- Keywords
- photoplethysmogram, arterial blood pressure estimation, non-invasive blood pressure monitoring, deep learning, systolic blood pressure, diastolic blood pressure, mean arterial pressure, AAMI standards
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