Multiscale Cross-Approximate Entropy Analysis of Bilateral Fingertips Photoplethysmographic Pulse Amplitudes among Middle-to-Old Aged Individuals with or without Type 2 Diabetes

Wu, Hsien‐Tsai; Yang, Cheng–Chan; Lin, Gen-Min; Haryadi, Bagus; Shiao-Chiang, Chu; Yang, Chieh-Ming; Sun, Cheuk‐Kwan

doi:10.3390/e19040145

Cited by 10 publications

(9 citation statements)

References 25 publications

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“…We have previously shown that discrepancy in DVP waveform amplitude between two upper limbs can differentiate young from aged subjects and patients with good from those with poor diabetic control as well as identify arteriosclerosis risks 15 . Using the multiscale entropy approach, DVP waveform amplitudes have also been demonstrated to differentiate healthy from diabetic subjects 16 , 17 .…”

Section: Discussionmentioning

confidence: 99%

Instantaneous frequency from Hilbert-Huang transformation of digital volume pulse as indicator of diabetes and arterial stiffness in upper-middle-aged subjects

Wei

Xiao

Chen

et al. 2018

Sci Rep

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To investigate the value of decomposed short-time digital volume pulse (DVP) signals in discerning systemic vascular anomaly in diabetic patients, demographic and anthropometric parameters, serum lipid profile, fasting blood glucose and glycated hemoglobin (HbA1c) levels were obtained from 29 healthy adults (Group 1) and 29 age-matched type 2 diabetes mellitus patients (Group 2). Six-second DVP signals from right index finger acquired through photoplethysmography were decomposed using ensemble empirical mode decomposition. Using one intrinsic mode function (IMF5), stiffness index (SI) and instantaneous energy of maximal energy (fEmax) were obtained. Other indicators of arterial stiffness, including electrocardiogram-pulse wave velocity of foot (ECG-PWVfoot), crest time (CT) and crest time ratio (CTR), were obtained from the testing subjects for comparison. The mean body weight, body mass index, waist circumference, HbA1c and fasting blood sugar levels were higher in Group 2 than those in Group 1, whereas values of systolic and diastolic blood pressure were lower in Group 2 than those in Group 1. SI and fEmax were significantly higher in Group 2 than those in Group 1. Moreover, fEmax was positively associated with HbA1c concentration, CT and SI in Group 2 (p < 0.05) but not in Group 1. When all subjects were considered, fEmax was highly significantly associated with HbA1c and fasting blood sugar levels, and SI (all p < 0.001). After Hilbert-Huang transformation, short-time DVP signals could give significant information on arterial stiffness and vascular anomaly in diabetic patients.

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Section: Discussionmentioning

confidence: 99%

Instantaneous frequency from Hilbert-Huang transformation of digital volume pulse as indicator of diabetes and arterial stiffness in upper-middle-aged subjects

Wei

Xiao

Chen

et al. 2018

Sci Rep

Self Cite

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“…The derivative operation of Equation (14) is processed to obtain the dual optimization problem as follows:…”

Section: Multiscale Dispersion Entropymentioning

confidence: 99%

“…However, AE is heavily relied on the data length. Moreover, its estimated value is uniformly lower than expected ones when processing the short dataset [14]. To overcome the weakness of AE, Richman and Moorman proposed sample entropy (SE) [15].…”

Section: Introductionmentioning

confidence: 98%

Partial Discharge Fault Diagnosis Based on Multi-Scale Dispersion Entropy and a Hypersphere Multiclass Support Vector Machine

Shang

Feng

2019

Entropy

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Partial discharge (PD) fault analysis is an important tool for insulation condition diagnosis of electrical equipment. In order to conquer the limitations of traditional PD fault diagnosis, a novel feature extraction approach based on variational mode decomposition (VMD) and multi-scale dispersion entropy (MDE) is proposed. Besides, a hypersphere multiclass support vector machine (HMSVM) is used for PD pattern recognition with extracted PD features. Firstly, the original PD signal is decomposed with VMD to obtain intrinsic mode functions (IMFs). Secondly proper IMFs are selected according to central frequency observation and MDE values in each IMF are calculated. And then principal component analysis (PCA) is introduced to extract effective principle components in MDE. Finally, the extracted principle factors are used as PD features and sent to HMSVM classifier. Experiment results demonstrate that, PD feature extraction method based on VMD-MDE can extract effective characteristic parameters that representing dominant PD features. Recognition results verify the effectiveness and superiority of the proposed PD fault diagnosis method.

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“…ApEn was proposed by Pincus [14,15] to quantitatively detect the regularity and complexity of various time series. Due to ApEn having the advantages of small amount of data, strong antinoise ability and accurate detection results, it has been widely used in the fields of medical treatment, stock markets, networks, meteorology and so on [16][17][18][19][20]. Sample entropy is an improved method of ApEn, which has great advantages in the abrupt analysis of hydrological series, but slightly lacks trend analysis for sequences [21].…”

Section: Introductionmentioning

confidence: 99%

Complexity Analysis of Precipitation and Runoff Series Based on Approximate Entropy and Extreme-Point Symmetric Mode Decomposition

Sun

Zhang

Guo

2018

Water

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Many regional hydrological regime changes are complex under the influences of climate change and human activities, which make it difficult to understand the regional or basin al hydrological status. To investigate the complexity of precipitation and the runoff time series from 1960 to 2012 in the Jing River Basin on different time scales, approximate entropy, a Bayesian approach and extreme-point symmetric mode decomposition were employed. The results show that the complexity of annual precipitation and runoff has decreased since the 1990sand that the change occurred in 1995. The Intrinsic Mode Function (IMF)-6 component decomposed by extreme-point symmetric mode decomposition of monthly precipitation and runoff was consistent with precipitation and runoff. The IMF-6 component of monthly precipitation closely followed the 10-year cycle of change, and it has an obvious correlation with sunspots. The correlation coefficient is 0.6, representing a positive correlation before 1995 and a negative correlation after 1995. However, the IMF-6 component of monthly runoff does not have a significant correlation with sunspots, and the correlation coefficient is only 0.41, which indicates that climate change is not the dominant factor of runoff change. Approximate entropy is an effective analytical method for complexity, and furthermore, it can be decomposed by extreme-point symmetric mode decomposition to obtain the physical process of the sequences at different time scales, which helps us to understand the background of climate change and human activity in the process of precipitation and runoff.

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Multiscale Cross-Approximate Entropy Analysis of Bilateral Fingertips Photoplethysmographic Pulse Amplitudes among Middle-to-Old Aged Individuals with or without Type 2 Diabetes

Cited by 10 publications

References 25 publications

Instantaneous frequency from Hilbert-Huang transformation of digital volume pulse as indicator of diabetes and arterial stiffness in upper-middle-aged subjects

Instantaneous frequency from Hilbert-Huang transformation of digital volume pulse as indicator of diabetes and arterial stiffness in upper-middle-aged subjects

Partial Discharge Fault Diagnosis Based on Multi-Scale Dispersion Entropy and a Hypersphere Multiclass Support Vector Machine

Complexity Analysis of Precipitation and Runoff Series Based on Approximate Entropy and Extreme-Point Symmetric Mode Decomposition

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