Blood Pressure Estimation from Photoplethysmogram Using a Spectro-Temporal Deep Neural Network
Open accessAbstract
Blood pressure (BP) is a direct indicator of hypertension, a dangerous and potentially deadly condition. Regular monitoring of BP is thus important, but many people have aversion towards cuff-based devices, and their limitation is that they can only be used at rest. Using just a photoplethysmogram (PPG) to estimate BP is a potential solution investigated in our study. We analyzed the MIMIC III database for high-quality PPG and arterial BP waveforms, resulting in over 700 h of signals after preprocessing, belonging to 510 subjects. We then used the PPG alongside its first and second derivative as inputs into a novel spectro-temporal deep neural network with residual connections. We have shown in a leave-one-subject-out experiment that the network is able to model the dependency between PPG and BP, achieving mean absolute errors of 9.43 for systolic and 6.88 for diastolic BP. Additionally we have shown that personalization of models is important and substantially improves the results, while deriving a good general predictive model is difficult. We have made crucial parts of our study, especially the list of used subjects and our neural network code, publicly available, in an effort to provide a solid baseline and simplify potential comparison between future studies on an explicit MIMIC III subset.
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Techniques that use a single PPG signal for BP estimation are based on analysis of pulse wave shape using either extracted features or the whole pulse wave.
“Techniques that use a single PPG signal are based on analysis of pulse wave shape using either extracted features [151]–[153] or the whole pulse wave [154].”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [154]
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- Authors
- Slapničar, G., Mlakar, N., & Luštrek, M.
- Affiliations
- Jožef Stefan Institute (SI)
- University of Ljubljana (SI)
- Corresponding author
- Slapničar, Gašper
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2019-08-04
- Volume
- 19
- Issue
- 15
- Pages
- 3420
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC6696196
- OpenAlex
- W2965846681
- Cited by
- 387
- References
- 26
- Citations by year
- 2026: 48 · 2025: 56 · 2024: 58 · 2023: 84 · 2022: 66 · 2021: 52 · 2020: 23
- FWCI
- 12.40
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Commission (689660)
- 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 › Cardiology and Cardiovascular Medicine › Blood Pressure and Hypertension Studies
- Keywords
- blood pressure estimation, photoplethysmogram, skip connections, hypertension monitoring, cuffless blood pressure measurement, model personalization, MIMIC-III
- MeSH terms
- Deep Learning, Blood Pressure, Humans, Signal Processing, Computer-Assisted, Neural Networks, Computer, Photoplethysmography
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