A fast sample entropy for pulse rate variability analysis

Chou, Lijuan; Gong, Shengrong; Yang, Haiping; Liu, Jicheng; Chou, Yongxin

doi:10.1007/s11517-022-02766-y

Cited by 4 publications

(2 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar to FD, sample entropy is a common non-linear dynamics metric that suffers from the inability to characterize multichannel time series information [15][16][17][18][19][20][21]. To overcome this problem, Ahmed and Mandic proposed multivariate multiscale sample entropy (MvmSE) for performing multiscale entropy analysis on multiple data channels simultaneously [22].…”

Section: Introductionmentioning

confidence: 99%

Multivariate Multiscale Higuchi Fractal Dimension and Its Application to Mechanical Signals

Li,

Zhang,

Liang

et al. 2024

Fractal Fract

View full text Add to dashboard Cite

Fractal dimension, as a common nonlinear dynamics metric, is extensively applied in biomedicine, fault diagnosis, underwater acoustics, etc. However, traditional fractal dimension can only analyze the complexity of the time series given a single channel at a particular scale. To characterize the complexity of multichannel time series, multichannel information processing was introduced, and multivariate Higuchi fractal dimension (MvHFD) was proposed. To further analyze the complexity at multiple scales, multivariate multiscale Higuchi fractal dimension (MvmHFD) was proposed by introducing multiscale processing algorithms as a technology that not only improved the use of fractal dimension in the analysis of multichannel information, but also characterized the complexity of the time series at multiple scales in the studied time series data. The effectiveness and feasibility of MvHFD and MvmHFD were verified by simulated signal experiments and real signal experiments, in which the simulation experiments tested the stability, computational efficiency, and signal separation performance of MvHFD and MvmHFD, and the real signal experiments tested the effect of MvmHFD on the recognition of multi-channel mechanical signals. The experimental results show that compared to other indicators, A achieves a recognition rate of 100% for signals in three features, which is at least 17.2% higher than for other metrics.

show abstract

Section: Introductionmentioning

confidence: 99%

Multivariate Multiscale Higuchi Fractal Dimension and Its Application to Mechanical Signals

Li,

Zhang,

Liang

et al. 2024

Fractal Fract

View full text Add to dashboard Cite

show abstract

“…Sample entropy (SE) is an effective nonlinear analysis method proposed by Chou et al to measure the complexity of signals [17]. It eliminates the impact of different signal lengths and pattern matching [18], making it more sensitive to the complexity of signal calculations [19,20].…”

Section: Introductionmentioning

confidence: 99%

Pipeline leak location method based on SSA-VMD with generalized quadratic cross-correlation^*

Peng,

Hu,

Zhang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Natural gas pipelines are an essential part of the economy. Natural gas pipelines may leak after aging, strong vibration signals may be generated in the pipeline when leakage occurs, and vibration signals may be noisy. Traditional variational mode decomposition (VMD) noise reduction methods need to set parameters in advance, and so may not achieve the best decomposition effect. To solve this problem, this paper proposes a method for pipeline leakage location based on the sparrow search algorithm (SSA) optimization of VMD combined with generalized quadratic cross-correlation. The method first calculates the original signal-to-noise ratio, and if the signal-to-noise ratio is low, wavelet threshold denoising is used to process the signal. Then, SSA optimization is used to refine the two key parameters of VMD (penalty parameter α and mode decomposition number K) based on sample entropy. Subsequently, the signal undergoes decomposition into K intrinsic mode function (IMF) components through VMD according to the obtained analysis parameter combination. Then, the IMF components are screened to obtain the reconstructed signal. Finally, the noise reduction signal is obtained. The signal delay after noise reduction is obtained through a generalized quadratic cross-correlation and the accurate leakage position is obtained using the delay. Experiments showed that the minimum relative error of this method could reach 0.6%, which was more accurate than the traditional VMD method, and effectively improved the accuracy of noisy signals in pipeline leakage locations.

show abstract

Research on heart rate extraction method based on mobile phone video

Yao,

Chou,

Yang

et al. 2023

Medical Engineering & Physics

View full text Add to dashboard Cite

A fast sample entropy for pulse rate variability analysis

Cited by 4 publications

References 37 publications

Multivariate Multiscale Higuchi Fractal Dimension and Its Application to Mechanical Signals

Multivariate Multiscale Higuchi Fractal Dimension and Its Application to Mechanical Signals

Pipeline leak location method based on SSA-VMD with generalized quadratic cross-correlation^*

Research on heart rate extraction method based on mobile phone video

Contact Info

Product

Resources

About

A fast sample entropy for pulse rate variability analysis

Cited by 4 publications

References 37 publications

Multivariate Multiscale Higuchi Fractal Dimension and Its Application to Mechanical Signals

Multivariate Multiscale Higuchi Fractal Dimension and Its Application to Mechanical Signals

Pipeline leak location method based on SSA-VMD with generalized quadratic cross-correlation*

Research on heart rate extraction method based on mobile phone video

Contact Info

Product

Resources

About

Pipeline leak location method based on SSA-VMD with generalized quadratic cross-correlation^*