Synthetic photoplethysmogram generation using two Gaussian functions
Open accessAbstract
Evaluating the performance of photoplethysmogram (PPG) event detection algorithms requires a large number of PPG signals with different noise levels and sampling frequencies. As publicly available PPG databases provide few options, artificially constructed PPG signals can also be used to facilitate this evaluation. Here, we propose a dynamic model to synthesize PPG over specified time durations and sampling frequencies. In this model, a single pulse was simulated by two Gaussian functions. Additionally, the beat-to-beat intervals were simulated using a normal distribution with a specific mean value and a specific standard deviation value. To add periodicity and to generate a complete signal, the circular motion principle was used. We synthesized three classes of pulses by emulating three different templates: excellent (systolic and diastolic waves are salient), acceptable (systolic and diastolic waves are not salient), and unfit (systolic and diastolic waves are noisy). The optimized model fitting of the Gaussian functions to the templates yielded 0.99, 0.98, and 0.85 correlations between the template and synthetic pulses for the excellent, acceptable, and unfit classes, respectively, with mean square errors of 0.001, 0.003, and 0.017, respectively. By comparing the heart rate variability of real PPG and randomly synthesized PPG for 5 min in 116 records from the MIMIC III database, strong correlations were found in SDNN, RMSSD, LF, HF, SD1, and SD2 (0.99, 0.89, 0.84, 0.89, 0.90 and 0.95, respectively).
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Other PPG simulators represented artifacts as additive harmonic signals.
“Such an approach excels other PPG simulators which generated stationary artifacts based on the characteristics of a single real artifact [33] or represented them as additive harmonic signals [56].”
From Modeling of artifacts in the wrist photoplethysmogram: Application to the detection of life-threatening arrhythmias (2021) · cites this paper as [56]
The developed artifact modeling approach excels other PPG simulators.
“Such an approach excels other PPG simulators which generated stationary artifacts based on the characteristics of a single real artifact [33] or represented them as additive harmonic signals [56].”
From Modeling of artifacts in the wrist photoplethysmogram: Application to the detection of life-threatening arrhythmias (2021) · cites this paper as [56]
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MIMIC-III, a freely accessible critical care database
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- Authors
- Tang, Q., Chen, Z., Ward, R., & Elgendi, M.
- Affiliations
- University of British Columbia (CA)
- Guilin University of Electronic Technology (CN)
- Corresponding author
- Chen, Zhencheng
- Elgendi, Mohamed
- Venue
- Scientific Reports
- Published
- 2020-08-17
- Volume
- 10
- Issue
- 1
- Pages
- 13883-13883
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC7431427
- OpenAlex
- W3049132276
- Cited by
- 62
- References
- 30
- Citations by year
- 2026: 8 · 2025: 12 · 2024: 9 · 2023: 12 · 2022: 13 · 2021: 7 · 2020: 1
- FWCI
- 2.59
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada (RGPIN-2014, 04462, RGPIN-2014-04462)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- photoplethysmogram, Gaussian fitting, heart rate variability, MIMIC-III
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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