Analysis and detection of motion artifact in photoplethysmographic data using higher order statistics
Abstract
In this work, we present novel methods for detecting the presence of motion artifact in photoplethysmographic (PPG) measurements based on higher order statistical information present in the data. We analyze both clean and corrupt PPG data in the time and frequency domains. In the time domain, skew and kurtosis measures of the signal are used as distinguishing metrics between clean and motion- corrupted data. In the frequency domain, the presence of random components due to motion artifact is analyzed using a frequency domain kurtosis measure. Additionally, bispectral analysis of PPG data indicates the presence of strong quadratic phase coupling (QPC) and more specifically self coupling in the case of clean PPG data. Though quadratic phase coupling is found in data corrupted by motion artifact, the self coupling feature is absent. A Neyman-Pearson (NP) detection rule is formulated for each of the measures. Additionally, treating each of the measures as observations from independent sensors, the Varshney-Chair rule is used to fuse individual decisions to form a global system decision.
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Krishnan et al. discovered that skew and kurtosis measurements in the time domain were higher when there was corruption, and frequency-domain kurtosis was smaller when there was corruption.
“They discovered skew and kurtosis measurements in the time domain to be higher when there was corruption, and the frequency-domain kurtosis was smaller when there was corruption.”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [173]
Quadratic phase was present in corrupted signals.
“The quadratic phase was present in corrupted signals.”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [173]
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Self-citationWhat should we say about the kurtosis?
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Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring
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- Authors
- Krishnan, R., Natarajan, B., & Warren, S.
- Affiliations
- Kansas State University (US)
- Venue
- 2008 IEEE International Conference on Acoustics, Speech and Signal Processing
- Publisher
- IEEE
- Published
- 2008-03
- Pages
- 613-616
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W1992974489
- Cited by
- 45
- References
- 10
- Citations by year
- 2025: 4 · 2024: 5 · 2023: 2 · 2022: 4 · 2021: 2 · 2020: 4 · 2019: 4 · 2018: 2 · 2017: 2 · 2016: 2 · 2015: 1 · 2014: 2 · 2013: 1 · 2012: 3
- FWCI
- 5.07
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- 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
- motion artifact detection, photoplethysmography, higher-order statistics, skewness, kurtosis, bispectral analysis, quadratic phase coupling, self-coupling, Neyman-Pearson detection, decision fusion, PPG signal quality
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