Empirical mode decomposition analysis of near-infrared spectroscopy muscular signals to assess the effect of physical activity in type 2 diabetic patients.

Type 2 diabetes is a metabolic disorder that may cause major problems to several physiological systems. Exercise has proven to be very effective in the prevention, management and improvement of this pathology in patients. Muscle metabolism is often studied with near-infrared spectroscopy (NIRS), a noninvasive technique that can measure changes in the concentration of oxygenated (O2Hb) and reduced hemoglobin (HHb) of tissues. These NIRS signals are highly non-stationary, non-Gaussian and nonlinear in nature. The empirical mode decomposition (EMD) is used as a nonlinear adaptive model to extract information present in the NIRS signals. NIRS signals acquired from the tibialis anterior muscle of controls and type 2 diabetic patients are processed by EMD to yield three intrinsic mode functions (IMF). The sample entropy (SE), fractal dimension (FD), and Hurst exponent (HE) are computed from these IMFs. Subjects are monitored at the beginning of the study and after one year of a physical training programme. Following the exercise programme, we observed an increase in the SE and FD and a decrease in the HE in all diabetic subjects. Our results show the influence of physical exercise program in improving muscle performance and muscle drive by the central nervous system in the patients. A multivariate analysis of variance performed at the end of the training programme also indicated that the NIRS metabolic patterns of controls and diabetic subjects are more similar than at the beginning of the study. Hence, the proposed EMD technique applied to NIRS signals may be very useful to gain a non-invasive understanding of the neuromuscular and vascular impairment in diabetic subjects.

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

Diabetes mellitus (DM) is a metabolic disorder that is widely rampant throughout the world population these days. The uncontrolled DM may lead to complications of eye, heart, kidney and nerves. The most common type of diabetes is the type 2 diabetes or insulin-resistant DM. Near-infrared spectroscopy (NIRS) technology is widely used in non-invasive monitoring of physiological signals. Three types of NIRS signals are used in this work: (i) variation in the oxygenated haemoglobin (O2Hb) concentration, (ii) deoxygenated haemoglobin (HHb), and (iii) ratio of oxygenated over the sum of the oxygenated and deoxygenated haemoglobin which is defined as: tissue oxygenation index (TOI) to analyze the effect of exercise on diabetes subjects. The NIRS signal has the characteristics of non-linearity and non-stationarity. Hence, the very small changes in this time series can be efficiently extracted using higher order statistics (HOS) method. Hence, in this work, we have used sample and HOS entropies to analyze these NIRS signals. These computer aided techniques will assist the clinicians to diagnose and 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 subjects increased the sample entropy of the NIRS signals, thus revealing a better muscle performance and an improved recruitment by the central nervous system. Moreover, after one year of physical therapy, diabetic subjects showed a NIRS muscular metabolic pattern that was not distinguished from that of controls. We believe that sample and bispectral entropy analysis is need when the aim is to compare the inner structure of the NIRS signals during muscle contraction, particularly when dealing with neuromuscular impairments.