The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Two-Stage Approach for Detection and Reduction of Motion Artifacts in Photoplethysmographic Data
Abstract
Corruption of photopleythysmograms (PPGs) by motion artifacts has been a serious obstacle to the reliable use of pulse oximeters for real-time, continuous state-of-health monitoring. In this paper, we propose an automated, two-stage PPG data processing method to minimize the effects of motion artifacts. The technique is based on our prior work related to motion artifact detection (stage 1) [R. Krishnan, B. Natarajan, and S. Warren, "Analysis and detection of motion artifacts in photoplethysmographic data using higher order statistics,'' in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP 2008), Las Vegas, Nevada, Apr. 2008, pp. 613-616] and motion artifact reduction (stage 2) [R. Krishnan, B. Natarajan, and S. Warren, "Motion artifact reduction in photoplethysmography using magnitude-based frequency domain independent component analysis,'' in Proc. 17th Int. Conf. Comput. Commun. Network, St. Thomas, Virgin Islands, Aug. 2008, pp. 1-5]. Regarding stage 1, we present novel and consistent techniques to detect the presence of motion artifact in PPGs given higher order statistical information present in the data. We analyze these data in the time and frequency domains (FDs) and identify metrics to distinguish between clean and motion-corrupted data. A Neyman-Pearson detection rule is formulated for each of the metrics. Furthermore, by treating each of the metrics as observations from independent sensors, we employ hard fusion and soft fusion techniques presented in [Z. Chair and P. Varshney, "Optimal data fusion in multiple sensor detection systems,'' IEEE Trans. Aerosp. Electron. Syst., AES, vol. 1, no. 1, pp. 98-101, Jan. 1986] and [C. C. Lee and J. J. Chao, "Optimum local decision space partitioning for distributed detection,'' IEEE Trans. Aerosp. Electron. Syst., AES, vol. 25, no. 7, pp. 536-544, Jul. 1989], respectively, in order to fuse individual decisions into a global system decision. For stage two, we propose a motion artifact reduction method that is effective even in the presence of severe subject movement. The approach involves an enhanced preprocessing unit consisting of a motion detection unit (MDU, developed in this paper), period estimation unit, and Fourier series reconstruction unit. The MDU identifies clean data frames versus those corrupted with motion artifacts. The period estimation unit determines the fundamental frequency of a corrupt frame. The Fourier series reconstruction unit reconstructs the final preprocessed signal by utilizing the spectrum variability of the pulse waveform. Preprocessed data are then fed to a magnitude-based FD independent component analysis unit. This helps reduce motion artifacts present at the frequencies of the reconstruction components. Experimental results are presented to demonstrate the efficacy of the overall motion artifact reduction method.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Fast and robust fixed-point algorithms for independent component analysis
Hyvarinen, A. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.761722 · Cited by 6,246
An Introduction to Signal Detection and Estimation
Poor, H. V. (1988) · Springer Texts in Electrical Engineering · doi:10.1007/978-1-4757-3863-6 · Cited by 3,149
An Introduction to Signal Detection and Estimation
Poor, H. V. (1994) · Springer Texts in Electrical Engineering · doi:10.1007/978-1-4757-2341-0 · Cited by 2,659
Mendel, J. M. (1991) · Proceedings of the IEEE · doi:10.1109/5.75086 · Cited by 1,810
Bispectrum estimation: A digital signal processing framework
Nikias, C. L., & Raghuveer, M. R. (1987) · Proceedings of the IEEE · doi:10.1109/proc.1987.13824 · Cited by 1,377
Higher order spectra analysis, a nonlinear signal processing framework
(1994) · Control Engineering Practice · doi:10.1016/0967-0661(94)90335-2 · Cited by 1,133
Optimal Data Fusion in Multiple Sensor Detection Systems
Chair, Z., & Varshney, P. K. (1986) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.1986.310699 · Cited by 1,130
An introduction to signal detection and estimation (2nd ed.)
Poor, H. V. (1994) · Springer eBooks · Cited by 1,008
A Fast Fixed-Point Algorithm for Independent Component Analysis of Complex Valued Signals
Bingham, E. (2000) · International Journal of Neural Systems · doi:10.1016/s0129-0657(00)00002-8 · Cited by 854
Blind separation of convolved mixtures in the frequency domain
Smaragdis, P. (1998) · Neurocomputing · doi:10.1016/s0925-2312(98)00047-2 · Cited by 776
Motion Artifact Reduction in Photoplethysmography Using Independent Component Analysis
Kim, B. S., & Yoo, S. K. (2006) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2005.869784 · Cited by 399
Use of the kurtosis statistic in the frequency domain as an aid in detecting random signals
Dwyer, R. F. (1984) · IEEE Journal of Oceanic Engineering · doi:10.1109/joe.1984.1145602 · Cited by 116
Higher-order spectral analysis of burst patterns in EEG
Muthuswamy, J., Sherman, D. L., & Thakor, N. V. (1999) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.736762 · Cited by 105
Yao, J., & Warren, S. (2005) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2005.1617256 · Cited by 83
Self-citationFoo, J. Y. A. (2006) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2006.01.001 · Cited by 71
Subband-based blind signal separationfor noisy speech recognition
Park, H., Jung, H., Lee, T.-W., & Lee, S.-Y. (1999) · Electronics Letters · doi:10.1049/el:19991358 · Cited by 53
What should we say about the kurtosis?
Mansour, A., & Jutten, C. (1999) · IEEE Signal Processing Letters · doi:10.1109/97.803435 · Cited by 47
Krishnan, R., Natarajan, B., & Warren, S. (2008) · 2008 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2008.4517684 · Cited by 45
Self-citationGraybeal, J. M., & Petterson, M. T. (2005) · doi:10.1109/iembs.2004.1404497 · Cited by 41
An Improved Pre-processing Approach for Photoplethysmographic Signal
Weng, J., Ye, Z., & Weng, J. (2005) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2005.1616337 · Cited by 28
Processing of Pulse Oximeter Data Using Discrete Wavelet Analysis
Lee, S., Ibey, B. L., Xü, W., Wilson, M. A., Ericson, M. N., & Coté, G. L. (2005) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2005.847538 · Cited by 27
Krishnan, R., Natarajan, B., & Warren, S. (2008) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn.2008.ecp.71 · Cited by 20
Self-citationIndependent component analysis and high-order statistics for automatic artifact rejection
Mammone, N., & Morabito, F. C. (2006) · IEEE International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2005.1556286 · Cited by 17
Pilgram, B., Castiglioni, P., Parati, G., & Rienzo, M. D. (1997) · Biological Cybernetics · doi:10.1007/s004220050341 · Cited by 8
A Small, High Fidelity Reflectance Pulse Oximeter
Thompson, D. E., & Warren, S. (2020) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--2793 · Cited by 8
Self-citation
Cited by
Toward ubiquitous blood pressure monitoring via pulse transit time: Theory and practice
Mukkamala, R., Hahn, J.-O., Inan, O. T., Mestha, L. K., Kim, C.-S., Toreyin, H., & Kyal, S. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2441951 · Cited by 935
“But, as is typical of noninvasive measurements, PPG waveforms are highly susceptible to measurement (e.g., motion) and physiologic (e.g., respiratory) artifact [51]–[56], [140].”
BackgroundMethodInfluentialSmartphone Sensors for Health Monitoring and Diagnosis
Majumder, S., & Deen, M. J. (2019) · Sensors · doi:10.3390/s19092164 · Cited by 435
Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring
Fine, J., Branan, K. L., Rodriguez, A. J., Boonya-ananta, T., Ajmal, Ramella-Roman, J. C., McShane, M. J., & Coté, G. L. (2021) · Biosensors · doi:10.3390/bios11040126 · Cited by 348
Optimal Signal Quality Index for Photoplethysmogram Signals
Elgendi, M. (2016) · Bioengineering · doi:10.3390/bioengineering3040021 · Cited by 320
Heart Rate Estimation From Wrist-Worn Photoplethysmography: A Review
Biswas, D., Simoes-Capela, N., Van Hoof, C., & Van Helleputte, N. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2914166 · Cited by 258
Metadata
- Authors
- Krishnan, R., Natarajan, B., & Warren, S.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2010-08
- Volume
- 57
- Issue
- 8
- Pages
- 1867-1876
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2101394537
- Cited by
- 263
- References
- 25
- FWCI
- 3.28
- Type
- journal article
- Language
- en
- Open access
- closed
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 ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
