Application of Multiscale Entropy in Assessing Plantar Skin Blood Flow Dynamics in Diabetics with Peripheral Neuropathy

Diabetic foot ulcer (DFU) is a common complication of diabetes mellitus, while tissue ischemia caused by impaired vasodilatory response to plantar pressure is thought to be a major factor of the development of DFUs, which has been assessed using various measures of skin blood flow (SBF) in the time or frequency domain. These measures, however, are incapable of characterizing nonlinear dynamics of SBF, which is an indicator of pathologic alterations of microcirculation in the diabetic foot. This study recruited 18 type 2 diabetics with peripheral neuropathy and eight healthy controls. SBF at the first metatarsal head in response to locally applied pressure and heating was measured using laser Doppler flowmetry. A multiscale entropy algorithm was utilized to quantify the regularity degree of the SBF responses. The results showed that during reactive hyperemia and thermally induced biphasic response, the regularity degree of SBF in diabetics underwent only small changes compared to baseline and significantly differed from that in controls at multiple scales (p < 0.05). On the other hand, the transition of regularity degree of SBF in diabetics distinctively differed from that in controls (p < 0.05). These findings indicated that multiscale entropy could provide a more comprehensive assessment of impaired microvascular reactivity in the diabetic foot compared to other entropy measures based on only a single scale, which strengthens the use of plantar SBF dynamics to assess the risk for DFU.

[1]  Vinzenz von Tscharner,et al.  Multi-scale transitions of fuzzy sample entropy of RR-intervals and their phase-randomized surrogates: A possibility to diagnose congestive heart failure , 2017, Biomed. Signal Process. Control..

[2]  D. Parthimos,et al.  Deterministic nonlinear features of cutaneous perfusion are lost in diabetic subjects with neuropathy. , 2011, Microvascular research.

[3]  V. Tscharner,et al.  Reshape scale method: A novel multi scale entropic analysis approach , 2013 .

[4]  F. Bowling,et al.  The diabetic foot in 2015: an overview , 2016, Diabetes/metabolism research and reviews.

[5]  A. Stefanovska,et al.  Low-frequency oscillations of the laser Doppler perfusion signal in human skin. , 2006, Microvascular research.

[6]  Nerrolyn Ramstrand,et al.  Incidence of Lower-Limb Amputation in the Diabetic and Nondiabetic General Population , 2009, Diabetes Care.

[7]  Fuyuan Liao,et al.  Using Modified Sample Entropy to Characterize Aging-Associated Microvascular Dysfunction , 2016, Front. Physiol..

[8]  S. Bus,et al.  Prevention of foot ulcers in the at‐risk patient with diabetes: a systematic review , 2016, Diabetes/metabolism research and reviews.

[9]  Fuyuan Liao,et al.  Using local scale exponent to characterize heart rate variability in response to postural changes in people with spinal cord injury , 2015, Front. Physiol..

[10]  Yih-Kuen Jan,et al.  Diabetic foot ulceration and amputation , 2012 .

[11]  Madalena Costa,et al.  Multiscale entropy analysis of biological signals. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  Fuyuan Liao,et al.  Relationship between nonlinear properties of sacral skin blood flow oscillations and vasodilatory function in people at risk for pressure ulcers. , 2010, Microvascular research.

[13]  Madalena Costa,et al.  Multiscale entropy analysis of complex physiologic time series. , 2002, Physical review letters.

[14]  Jean-Luc Cracowski,et al.  Methodological issues in the assessment of skin microvascular endothelial function in humans. , 2006, Trends in pharmacological sciences.

[15]  W. Jeffcoate,et al.  Variation in the recorded incidence of amputation of the lower limb in England , 2012, Diabetologia.

[16]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[17]  Anne Humeau-Heurtier,et al.  The Multiscale Entropy Algorithm and Its Variants: A Review , 2015, Entropy.

[18]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[19]  A. Stefanovska,et al.  Wavelet analysis of oscillations in the peripheral blood circulation measured by laser Doppler technique , 1999, IEEE Transactions on Biomedical Engineering.

[20]  J. Mills.  Lower limb ischaemia in patients with diabetic foot ulcers and gangrene: recognition, anatomic patterns and revascularization strategies , 2016, Diabetes/metabolism research and reviews.

[21]  Wangxin Yu,et al.  Characterization of Surface EMG Signal Based on Fuzzy Entropy , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[22]  Fuyuan Liao,et al.  Nonlinear dynamics of skin blood flow response to mechanical and thermal stresses in the plantar foot of diabetics with peripheral neuropathy. , 2017, Clinical hemorheology and microcirculation.

[23]  Yih-Kuen Jan,et al.  Skin blood flow response to locally applied mechanical and thermal stresses in the diabetic foot. , 2013, Microvascular research.

[24]  Pere Caminal,et al.  Refined Multiscale Entropy: Application to 24-h Holter Recordings of Heart Period Variability in Healthy and Aortic Stenosis Subjects , 2009, IEEE Transactions on Biomedical Engineering.