AFODSS: Heart Rate Estimation Method From Photoplethysmographic Signals With Motion Artifacts Using Fourier-Sparse Dual Optimization

Precise heart rate (HR) estimation of individuals during physical exercise from PPG sensors using direct methods often produces erroneous output due to the occurrence of exceedingly high motion artifacts (MAs). The effect of MAs can be neutralized using non-direct algorithms such as optimization tec...

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Bibliographic Details
Published in:IEEE sensors journal 2019-11, Vol.19 (21), p.9953-9963
Main Authors: Raj, Remya, Selvakumar, J., Maik, Vivek
Format: Article
Language:English
Subjects:
Acceleration
Adaptive Fourier Decomposition
Algorithms
Bi-Conjugate Gradient
Biomedical monitoring
Estimation
FOCUSS Method
Fourier series
Heart rate
Multi Measurement Model(MMV)
Optimization
Optimization techniques
Photoplethysmography
Physical exercise
Sensors
Signal processing algorithms
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Summary:Precise heart rate (HR) estimation of individuals during physical exercise from PPG sensors using direct methods often produces erroneous output due to the occurrence of exceedingly high motion artifacts (MAs). The effect of MAs can be neutralized using non-direct algorithms such as optimization techniques using some a priori about MA. In this paper, we propose a dual-optimization technique using adaptive Fourier decomposition in conjunction with sparse spectral reconstruction (AFODSS) using a multiple measurement vectors (MMVs) model and a regularized bi-conjugate gradient focal underdetermined system solver (BCG-FOCUSS) algorithm. Using AFODSS, the average absolute error (AAE) of HR obtained on the dataset recorded from 23 subjects during their intense exercise is 1.35 beats/min. The BCG-FOCUSS algorithm used in this paper helps to speed up the convergence rate and reduce time complexity when compared with other FOCUSS algorithms.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2019.2928643