Photoplethysmogram signal quality estimation using repeated Gaussian filters and cross-correlation
Abstract
Pulse oximeters are monitors that noninvasively measure heart rate and blood oxygen saturation (SpO2). Unfortunately, pulse oximetry is prone to artifacts which negatively impact the accuracy of the measurement and can cause a significant number of false alarms. We have developed an algorithm to segment pulse oximetry signals into pulses and estimate the signal quality in real time. The algorithm iteratively calculates a signal quality index (SQI) ranging from 0 to 100. In the presence of artifacts and irregular signal morphology, the algorithm outputs a low SQI number. The pulse segmentation algorithm uses the derivative of the signal to find pulse slopes and an adaptive set of repeated Gaussian filters to select the correct slopes. Cross-correlation of consecutive pulse segments is used to estimate signal quality. Experimental results using two different benchmark data sets showed a good pulse detection rate with a sensitivity of 96.21% and a positive predictive value of 99.22%, which was equivalent to the available reference algorithm. The novel SQI algorithm was effective and produced significantly lower SQI values in the presence of artifacts compared to SQI values during clean signals. The SQI algorithm may help to guide untrained pulse oximeter users and also help in the design of advanced algorithms for generating smart alarms.
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Among several pulse-to-pulse SQIs, only the one in [10] has been tested in long-term wrist PPGs acquired outside the laboratory setting.
“Out of several pulse-to-pulse SQIs [10,[49][50][51], only the one in [10] has been tested in long-term wrist PPGs acquired outside the laboratory setting and was therefore implemented in this study.”
From Modeling of artifacts in the wrist photoplethysmogram: Application to the detection of life-threatening arrhythmias (2021) · cites this paper as [50]
Karlen et al. created an algorithm with 96.21% sensitivity and 99.2% positive predictive value for good pulses using repeated Gaussian filters and cross-correlation.
“Karlen et al. created an algorithm with 96.21% sensitivity and 99.2% positive predictive value for good pulses using repeated Gaussian filters and cross-correlation [168].”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [168]
With this algorithm, Karlen et al. were able to distinguish where motion artifacts occurred in a beat-to-beat manner.
“With this algorithm, Karlen et al. were able to distinguish where motion artifacts occurred in a beat-to-beat manner.”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [168]
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- Authors
- Karlen, W., Kobayashi, K., Ansermino, J. M., & Dumont, G. A.
- Affiliations
- University of British Columbia (CA)
- Corresponding author
- Karlen, W
- Venue
- Physiological Measurement
- Publisher
- IOP Publishing
- Published
- 2012-09-18
- Volume
- 33
- Issue
- 10
- Pages
- 1617-1629
- ISSN
- 0967-3334 (ISSN-L)
- 1361-6579
- OpenAlex
- W2160347936
- Cited by
- 123
- References
- 18
- Citations by year
- 2026: 10 · 2025: 8 · 2024: 7 · 2023: 10 · 2022: 12 · 2021: 11 · 2020: 10 · 2019: 11 · 2018: 13 · 2017: 7 · 2016: 9 · 2015: 6 · 2014: 6 · 2013: 3
- FWCI
- 2.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Physiological Measurement (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Hirosaki University
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Keywords
- photoplethysmogram, pulse oximetry, signal quality estimation, pulse detection, Gaussian filtering, cross correlation function, signal artifacts, real-time monitoring, smart alarms, heart rate monitoring
- MeSH terms
- Adolescent, Adult, Aged, Algorithms, Child, Child, Preschool, Humans, Infant, Middle Aged, Quality Control, Signal Processing, Computer-Assisted, Normal Distribution, Databases, Factual, Photoplethysmography, Young Adult
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