Classification of glucose records from patients at diabetes risk using a combined permutation entropy algorithm

Cuesta–Frau, David; Miró-Martínez, Pau; Oltra-Crespo, Sandra; Jordán-Núñez, Jorge; Vargas, Borja; Vigil, Luis

doi:10.1016/j.cmpb.2018.08.018

Cited by 17 publications

(18 citation statements)

References 43 publications

(92 reference statements)

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“…It is important to note that some configuration parameters did not yield significant results, such as = 3 for PE (Table 6) and mPE ( Table 7). As in previous similar studies [37][38][39], it seems the greater the embedded dimension , the better classification performance using PE-based measures.…”

Section: Resultssupporting

confidence: 76%

Influence of Duodenal–Jejunal Implantation on Glucose Dynamics: A Pilot Study Using Different Nonlinear Methods

et al. 2019

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Diabetes is a disease of great and rising prevalence, with the obesity epidemic being a significant contributing risk factor. Duodenal–jejunal bypass liner (DJBL) is a reversible implant that mimics the effects of more aggressive surgical procedures, such as gastric bypass, to induce weight loss. We hypothesized that DJBL also influences the glucose dynamics in type II diabetes, based on the induced changes already demonstrated in other physiological characteristics and parameters. In order to assess the validity of this assumption, we conducted a quantitative analysis based on several nonlinear algorithms (Lempel–Ziv Complexity, Sample Entropy, Permutation Entropy, and modified Permutation Entropy), well suited to the characterization of biomedical time series. We applied them to glucose records drawn from two extreme cases available of DJBL implantation: before and after 10 months. The results confirmed the hypothesis and an accuracy of 86.4% was achieved with modified Permutation Entropy. Other metrics also yielded significant classification accuracy results, all above 70%, provided a suitable parameter configuration was chosen. With the Leave–One–Out method, the results were very similar, between 72% and 82% classification accuracy. There was also a decrease in entropy of glycaemia records during the time interval studied. These findings provide a solid foundation to assess how glucose metabolism may be influenced by DJBL implantation and opens a new line of research in this field.

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Section: Resultssupporting

confidence: 76%

Influence of Duodenal–Jejunal Implantation on Glucose Dynamics: A Pilot Study Using Different Nonlinear Methods

et al. 2019

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“…Permutation entropy has been widely Complexity 3 used in medicine, meteorology, and other fields, and now it is gradually being applied to mechanical fault diagnosis. The basic algorithm for permutation entropy is [36] (1) Phase Space Reconstruction of Time Series. A phase sequence can be obtained by performing phase space reconstruction on a time series { ( ), = 1, 2, .…”

Section: Permutation Entropy Permutation Entropy (Pe) Is Anmentioning

confidence: 99%

Application of an Improved Ensemble Local Mean Decomposition Method for Gearbox Composite Fault Diagnosis

Wang

Cai

et al. 2019

Complexity

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In industrial production, it is highly essential to extract faults in gearbox accurately. Specifically, in a strong noise environment, it is difficult to extract the fault features accurately. LMD (local mean decomposition) is widely used as an adaptive decomposition method in fault diagnosis. In order to improve the mode mixing of LMD, ELMD (ensemble Local Mean Decomposition) is proposed as local mode mixing exists in noisy environment, but white noise added in ELMD cannot be completely neutralized leading to the influence of increased white noise on PF (product function) component. This further leads to the increase in reconstruction errors. Therefore, this paper proposes a composite fault diagnosis method for gearboxes based on an improved ensemble local mean decomposition. The idea is to add white noise in pairs to optimize ELMD, defined as CELMD (Complementary Ensemble Local Mean Decomposition) then remove the decomposed high noise component by PE (Permutation Entropy) while applying the SG (Savitzky-Golay) filter to smooth out the low noise in PFs. The method is applied to both simulated signal and experimental signal, which overcomes mode mixing phenomenon and reduces reconstruction error. At the same time, this method avoids the occurrence of pseudocomponents and reduces the amount of calculation. Compared with LMD, ELMD, CELMD, and CELMDAN, it shows that improved ensemble local mean decomposition method is an effective method for extracting composite fault features.

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“…Therefore, only relatively small values of the embedded dimension m should be used, in accordance with the recommendation stated above. Unfortunately, high values of m usually provide better signal classification performance [ 24 , 25 , 26 ], and this fact leads to an antagonistic and counterproductive relationship between PE stability, and its segmentation power. For example, in reference [ 24 ], the classification performance of PE using electroencephalogram records of 4096 samples, temperature records of 480 samples, RR records of some 1000 samples, and continuous glucose monitoring records of 280 samples was analysed.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, there are studies where, despite analysing short time series with high m values that did not fulfil the relationship

, the classification achieved using PE was very good [ 24 , 26 , 27 ]. This led to the hypothesis that PE probably achieves stability before it was initially thought, especially for larger m values, and additionally, such stability is not required to attain a significant classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Embedded Dimension and Time Series Length. Practical Influence on Permutation Entropy and Its Applications

Cuesta–Frau

Murillo-Escobar

Orrego

et al. 2019

Entropy

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Permutation Entropy (PE) is a time series complexity measure commonly used in a variety of contexts, with medicine being the prime example. In its general form, it requires three input parameters for its calculation: time series length N, embedded dimension m, and embedded delay τ . Inappropriate choices of these parameters may potentially lead to incorrect interpretations. However, there are no specific guidelines for an optimal selection of N, m, or τ , only general recommendations such as N > > m ! , τ = 1 , or m = 3 , … , 7 . This paper deals specifically with the study of the practical implications of N > > m ! , since long time series are often not available, or non-stationary, and other preliminary results suggest that low N values do not necessarily invalidate PE usefulness. Our study analyses the PE variation as a function of the series length N and embedded dimension m in the context of a diverse experimental set, both synthetic (random, spikes, or logistic model time series) and real–world (climatology, seismic, financial, or biomedical time series), and the classification performance achieved with varying N and m. The results seem to indicate that shorter lengths than those suggested by N > > m ! are sufficient for a stable PE calculation, and even very short time series can be robustly classified based on PE measurements before the stability point is reached. This may be due to the fact that there are forbidden patterns in chaotic time series, not all the patterns are equally informative, and differences among classes are already apparent at very short lengths.

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Classification of glucose records from patients at diabetes risk using a combined permutation entropy algorithm

Cited by 17 publications

References 43 publications

Influence of Duodenal–Jejunal Implantation on Glucose Dynamics: A Pilot Study Using Different Nonlinear Methods

Influence of Duodenal–Jejunal Implantation on Glucose Dynamics: A Pilot Study Using Different Nonlinear Methods

Application of an Improved Ensemble Local Mean Decomposition Method for Gearbox Composite Fault Diagnosis

Embedded Dimension and Time Series Length. Practical Influence on Permutation Entropy and Its Applications

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