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Proceedings ArticleDOI

Finger photoplethysmogram signal enhancement: Comparing performance between PCA and ICA methods

01 Apr 2017-pp 203-209

TL;DR: This paper compares the performance between two popular statistical signal processing tools, viz., principal component analysis (PCA) with fast independent component analysis (/ICA) in reduction of MA from finger pulse signal collected from 30 human volunteers and found that beat to beat correlation is higher in the PCA preprocessed data.

AbstractPulse signal is prone to corruption with motion artifacts (MA) due to attachment of the sensor to extreme body parts like finger, toes and forehead. This paper compares the performance between two popular statistical signal processing tools, viz., principal component analysis (PCA) with fast independent component analysis (/ICA) in reduction of MA from finger pulse signal collected from 30 human volunteers. A multivariate dataset was generated with systolic peak-aligned Photoplethysmogram (PPG) beats extracted from time series data. After eigenvalues decomposition of the covariance matrix, the original data was reconstructed using the first principal component. The mean correlation coefficient of average beat template of ICA preprocessed data and clean data, averaged over 30 volunteers is 0.9876 while that of PCA preprocessed data with clean data is 0.9778. With white Gaussian noise of known SNR, maximum absolute error for PCA preprocessed data is very small, 3.14% from SNR 25dB onwards. It was also found that beat to beat correlation is higher in the PCA preprocessed data.

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Citations
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Journal ArticleDOI
Abstract: Non-invasive photoplethysmography (PPG) technology was developed to track heart rate during motion. Automated analysis of PPG has made it useful in both clinical and non-clinical applications. However, PPG-based heart rate tracking is a challenging problem due to motion artifacts (MAs) which are main contributors towards signal degradation as they mask the location of heart rate peak in the spectra. A practical analysis system must have good performance in MA removal as well as in tracking. In this article, we have presented state-of-art techniques in both areas of the automated analysis, i.e., MA removal and heart rate tracking, and have concluded that adaptive filtering and multi-resolution decomposition techniques are better for MA removal and machine learning-based approaches are future perspective of heart rate tracking. Hence, future systems will be composed of machine learning-based trackers fed with either empirically decomposed signal or from output of adaptive filter.

9 citations


References
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Journal ArticleDOI
TL;DR: Using maximum entropy approximations of differential entropy, a family of new contrast (objective) functions for ICA enable both the estimation of the whole decomposition by minimizing mutual information, and estimation of individual independent components as projection pursuit directions.
Abstract: Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. We use a combination of two different approaches for linear ICA: Comon's information theoretic approach and the projection pursuit approach. Using maximum entropy approximations of differential entropy, we introduce a family of new contrast functions for ICA. These contrast functions enable both the estimation of the whole decomposition by minimizing mutual information, and estimation of individual independent components as projection pursuit directions. The statistical properties of the estimators based on such contrast functions are analyzed under the assumption of the linear mixture model, and it is shown how to choose contrast functions that are robust and/or of minimum variance. Finally, we introduce simple fixed-point algorithms for practical optimization of the contrast functions.

5,716 citations


"Finger photoplethysmogram signal en..." refers methods in this paper

  • ...The fICA algorithm [21] was implemented on the array Z to extract the independent components....

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Journal ArticleDOI
John F. Allen1
TL;DR: Photoplethysmography is a simple and low-cost optical technique that can be used to detect blood volume changes in the microvascular bed of tissue and is often used non-invasively to make measurements at the skin surface.
Abstract: Photoplethysmography (PPG) is a simple and low-cost optical technique that can be used to detect blood volume changes in the microvascular bed of tissue. It is often used non-invasively to make measurements at the skin surface. The PPG waveform comprises a pulsatile ('AC') physiological waveform attributed to cardiac synchronous changes in the blood volume with each heart beat, and is superimposed on a slowly varying ('DC') baseline with various lower frequency components attributed to respiration, sympathetic nervous system activity and thermoregulation. Although the origins of the components of the PPG signal are not fully understood, it is generally accepted that they can provide valuable information about the cardiovascular system. There has been a resurgence of interest in the technique in recent years, driven by the demand for low cost, simple and portable technology for the primary care and community based clinical settings, the wide availability of low cost and small semiconductor components, and the advancement of computer-based pulse wave analysis techniques. The PPG technology has been used in a wide range of commercially available medical devices for measuring oxygen saturation, blood pressure and cardiac output, assessing autonomic function and also detecting peripheral vascular disease. The introductory sections of the topical review describe the basic principle of operation and interaction of light with tissue, early and recent history of PPG, instrumentation, measurement protocol, and pulse wave analysis. The review then focuses on the applications of PPG in clinical physiological measurements, including clinical physiological monitoring, vascular assessment and autonomic function.

2,489 citations

21 Feb 2007

704 citations


"Finger photoplethysmogram signal en..." refers background in this paper

  • ...Over the last decade, PPG technology has got significant importance from the medical science and biomedical research community [2-3]....

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Journal ArticleDOI

387 citations


"Finger photoplethysmogram signal en..." refers background in this paper

  • ...With each cardiac cycle, there is a momentary increase in the blood volume change in the blood capillary in peripheral arteries, which is captured by difference in received light intensity in the photodiode [1]....

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Journal ArticleDOI
TL;DR: The motion artifacts were reduced by exploiting the quasi-periodicity of the PPG signal and the independence between the P PG and the motion artifact signals by the combination of independent component analysis and block interleaving with low-pass filtering.
Abstract: Removing the motion artifacts from measured photoplethysmography (PPG) signals is one of the important issues to be tackled for the accurate measurement of arterial oxygen saturation during movement. In this paper, the motion artifacts were reduced by exploiting the quasi-periodicity of the PPG signal and the independence between the PPG and the motion artifact signals. The combination of independent component analysis and block interleaving with low-pass filtering can reduce the motion artifacts under the condition of general dual-wavelength measurement. Experiments with synthetic and real data were performed to demonstrate the efficacy of the proposed algorithm.

343 citations


"Finger photoplethysmogram signal en..." refers methods in this paper

  • ...A major focus of medical signal processing research involving PPG has been directed towards PPG enhancement, which includes adaptive filters [8], wavelet decomposition methods [9], cycle to cycle Fourier series analysis [10], independent component analysis (ICA) [11][12], and emperical mode decomposition [13]....

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