Comparison of wavelet transformation and adaptive filtering in restoring artefact-induced time-related measurement

Foo, Jong Yong Abdiel

doi:10.1016/j.bspc.2006.01.001

Cited by 64 publications

(30 citation statements)

References 15 publications

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“…The relevant parameters in the experiments were adjusted as follows: the spectrum grid parameter in DFT was set to N = 2048, the smoothing parameter was set to λ = 10 2 , the weighting parameter in (3) to p = 10 −3 , and the parameter in (6) to δ = 0.7. In the peak selection part, the parameters of the Laplacian distribution function were set to µ = 0 and b = 0.02.…”

Section: A Data Descriptionmentioning

confidence: 99%

Photoplethysmography-Based Heart Rate Monitoring Using Asymmetric Least Squares Spectrum Subtraction and Bayesian Decision Theory

2015

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Motion artifacts (MA) are strong interference sources in wearable photoplethysmography (PPG) signals, significantly affecting estimation of heart rate (HR) and other physiological parameters. In this study, a novel method called SPECTRAP is proposed for accurate motion-tolerant estimation of HR using a PPG signal and a simultaneous acceleration signal. The method first calculates the spectra of the PPG signal and the acceleration signal, and then removes the MA spectral components from the PPG spectrum using a new spectrum subtraction algorithm. The new spectrum subtraction algorithm is based on asymmetric least square and overcomes drawbacks of conventional spectrum subtraction algorithms. To find the spectral peak corresponding to HR on the resulting spectrum, SPECTRAP formulates the problem into a pattern classification problem and uses the Bayesian decision theory to solve it. Experimental results on the PPG database used in 2015 IEEE Signal Processing Cup showed that the proposed algorithm has excellent performance. The average absolute error on 12 training sets was 1.50 beat per minute (BPM) (standard deviation: 1.95 BPM). The average absolute error on the 10 testing sets was 2.13 BPM (standard deviation: 2.77 BPM).

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Section: A Data Descriptionmentioning

confidence: 99%

Photoplethysmography-Based Heart Rate Monitoring Using Asymmetric Least Squares Spectrum Subtraction and Bayesian Decision Theory

2015

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“…However, PPG signals acquired in such ambulatory settings are often distorted by motion artifacts which seriously hamper the above mentioned analysis procedures [2] [8]. Many contributions that focus on artifact reduction have been presented since, including methods based on adaptive noise cancellators [9] [10] [11], filter banks [12], Fourier analysis [13], Kalman filters [14], wavelets [15] 1 M. Pflugradt [16], smoothed Wigner-Ville distribution [17], singular value decomposition [18] or independent component analysis [19] [20].…”

Section: Introductionmentioning

confidence: 99%

Improved signal quality indication for photoplethysmographic signals incorporating motion artifact detection

Pflugradt

Orglmeister

2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Wearable monitoring systems have gained tremendous popularity in the health-care industry, opening new possibilities in diagnostic routines and medical treatments. Numerous hardware systems have been presented since, which allow for continuous acquisition of various biosignals like the ECG, PPG, EMG or EEG and which are suited for ambulatory settings. Unfortunately, these flexible systems are liable to motion artifacts and especially photoplethysmographic signals are seriously distorted when the patient is not at rest. A lot of work has been done to reduce artifacts and noise, ranging from simple filtering methods to very complex statistical approaches. With regard to the PPG, certain quality indices have been proposed to evaluate the signal conditions. As movements are the primary source of signal disturbances, the relation between the output of a signal quality estimator and acceleration data captured directly on the PPG sensor is focused in this work. It will be shown that typical motions can be detected on-line, thereby providing additional information which will significantly improve signal quality assessments.

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“…Unfortunately, the quality of PPG sensor signals can be easily affected by motion artifacts during intensive physical exercise. Therefore, the motion artifacts must be removed to measure heart rate accurately [3,4,5]. …”

Section: Introductionmentioning

confidence: 99%

SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals

Xiong

Cai

Wang

et al. 2017

Sensors

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Although wrist-type photoplethysmographic (hereafter referred to as WPPG) sensor signals can measure heart rate quite conveniently, the subjects’ hand movements can cause strong motion artifacts, and then the motion artifacts will heavily contaminate WPPG signals. Hence, it is challenging for us to accurately estimate heart rate from WPPG signals during intense physical activities. The WWPG method has attracted more attention thanks to the popularity of wrist-worn wearable devices. In this paper, a mixed approach called Mix-SVM is proposed, it can use multi-channel WPPG sensor signals and simultaneous acceleration signals to measurement heart rate. Firstly, we combine the principle component analysis and adaptive filter to remove a part of the motion artifacts. Due to the strong relativity between motion artifacts and acceleration signals, the further denoising problem is regarded as a sparse signals reconstruction problem. Then, we use a spectrum subtraction method to eliminate motion artifacts effectively. Finally, the spectral peak corresponding to heart rate is sought by an SVM-based spectral analysis method. Through the public PPG database in the 2015 IEEE Signal Processing Cup, we acquire the experimental results, i.e., the average absolute error was 1.01 beat per minute, and the Pearson correlation was 0.9972. These results also confirm that the proposed Mix-SVM approach has potential for multi-channel WPPG-based heart rate estimation in the presence of intense physical exercise.

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Comparison of wavelet transformation and adaptive filtering in restoring artefact-induced time-related measurement

Cited by 64 publications

References 15 publications

Photoplethysmography-Based Heart Rate Monitoring Using Asymmetric Least Squares Spectrum Subtraction and Bayesian Decision Theory

Photoplethysmography-Based Heart Rate Monitoring Using Asymmetric Least Squares Spectrum Subtraction and Bayesian Decision Theory

Improved signal quality indication for photoplethysmographic signals incorporating motion artifact detection

SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals

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