Diabetes mellitus (DM) is a metabolic disorder that is widely rampant throughout the worldpopulation these days. The uncontrolled DM may lead to complications of eye, heart, kidneyand nerves. The most common type of diabetes is the type 2 diabetes or insulin-resistantDM.Near-infrared spectroscopy (NIRS) technology is widely used in non-invasive monitoringof physiological signals. Three types of NIRS signals are used in this work: (i) variation in theoxygenated haemoglobin (O2Hb) concentration, (ii) deoxygenated haemoglobin (HHb), and(iii) ratio of oxygenated over the sum of the oxygenated and deoxygenated haemoglobinwhich is defined as: tissue oxygenation index (TOI) to analyze the effect of exercise ondiabetes subjects.The NIRS signal has the characteristics of non-linearity and non-stationarity. Hence, thevery small changes in this time series can be efficiently extracted using higher order statis-tics (HOS) method. Hence, in this work, we have used sample and HOS entropies to analyzethese NIRS signals. These computer aided techniques will assist the clinicians to diagnoseand monitor the health accurately and easily without any inter or intra observer variability.Results showed that after a one-year of physical exercise programme, all diabetic subjectsincreased the sample entropy of the NIRS signals, thus revealing a better muscle perfor-mance and an improved recruitment by the central nervous system. Moreover, after oneyear of physical therapy, diabetic subjects showed a NIRS muscular metabolic pattern thatwas not distinguished from that of controls.We believe that sample and bispectral entropy analysis is need when the aim is to comparethe inner structure of the NIRS signals during muscle contraction, particularly when dealingwith neuromuscular impairments.

Entropy analysis of muscular near-infrared spectroscopy (NIRS) signals during exercise programme of type 2 diabetic patients: Quantitative assessment of muscle metabolic pattern / Molinari, Filippo; Acharya, Ur; Martis, Rj; De Luca, R; Petraroli, G; Liboni, William. - In: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE. - ISSN 0169-2607. - 112:(2013), pp. 518-528. [10.1016/j.cmpb.2013.08.018]

Entropy analysis of muscular near-infrared spectroscopy (NIRS) signals during exercise programme of type 2 diabetic patients: Quantitative assessment of muscle metabolic pattern.

MOLINARI, FILIPPO;LIBONI, WILLIAM
2013

Abstract

Diabetes mellitus (DM) is a metabolic disorder that is widely rampant throughout the worldpopulation these days. The uncontrolled DM may lead to complications of eye, heart, kidneyand nerves. The most common type of diabetes is the type 2 diabetes or insulin-resistantDM.Near-infrared spectroscopy (NIRS) technology is widely used in non-invasive monitoringof physiological signals. Three types of NIRS signals are used in this work: (i) variation in theoxygenated haemoglobin (O2Hb) concentration, (ii) deoxygenated haemoglobin (HHb), and(iii) ratio of oxygenated over the sum of the oxygenated and deoxygenated haemoglobinwhich is defined as: tissue oxygenation index (TOI) to analyze the effect of exercise ondiabetes subjects.The NIRS signal has the characteristics of non-linearity and non-stationarity. Hence, thevery small changes in this time series can be efficiently extracted using higher order statis-tics (HOS) method. Hence, in this work, we have used sample and HOS entropies to analyzethese NIRS signals. These computer aided techniques will assist the clinicians to diagnoseand monitor the health accurately and easily without any inter or intra observer variability.Results showed that after a one-year of physical exercise programme, all diabetic subjectsincreased the sample entropy of the NIRS signals, thus revealing a better muscle perfor-mance and an improved recruitment by the central nervous system. Moreover, after oneyear of physical therapy, diabetic subjects showed a NIRS muscular metabolic pattern thatwas not distinguished from that of controls.We believe that sample and bispectral entropy analysis is need when the aim is to comparethe inner structure of the NIRS signals during muscle contraction, particularly when dealingwith neuromuscular impairments.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2518922
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