Cited by 390Non-Invasive Vital Sign MonitoringCardiovascular Health and Disease PreventionBlood Pressure and Hypertension Studies

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Blood Pressure Estimation from Photoplethysmogram Using a Spectro-Temporal Deep Neural Network

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Slapničar, Gašper, Mlakar, Nejc, Luštrek, Mitja·2019·Sensors

DOI 10.3390/s19153420PMC PMC6696196

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Abstract

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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Figure 1 Ideal photoplethysmogram (PPG) cycle waveform and its first and second derivatives next to distorted waveforms. The ideal example has a single large systolic peak and a single lower diastolic peak afterwards, while the anomalies have too many or too few peaks. All data is taken from the MIMIC III database [11].CC BY · PubMed Central

Figure 2 Flat lines anomaly can be observed in the arterial blood pressure (ABP) signal.CC BY · PubMed Central

Figure 3 Flat peaks anomaly can be observed in the ABP signal.CC BY · PubMed Central

Figure 4 Schematic pipeline of our system.CC BY · PubMed Central

Figure 5 Distributions of systolic blood pressure (SBP) and distolic blood pressure (DBP) in our final data.CC BY · PubMed Central

Figure 6 Schematic showing of our neural network architecture.CC BY · PubMed Central

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Authors: Slapničar, G., Mlakar, N., & Luštrek, M.
Affiliations: Jožef Stefan Institute (SI)
University of Ljubljana (SI)
Corresponding author: Slapničar, Gašper
ORCID: Slapničar, Gašper
Luštrek, Mitja
Venue: Sensors
Publisher: MDPI AG
Published: 2019-08-04
Volume: 19
Issue: 15
Pages: 3420
ISSN: 1424-8220 (ISSN-L)
DOI: 10.3390/s19153420
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: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
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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Blood Pressure Estimation from Photoplethysmogram Using a Spectro-Temporal Deep Neural Network

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