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

Authors:
Filippo Molinari;U. Rajendra Acharya;Roshan Joy Martis;Riccardo De Luca;Giuliana Petraroli;William Liboni
Affiliations:
-;-;-;-;-;-
Venue:
Computer Methods and Programs in Biomedicine
Year:
2013

Citing 7
Cited 0

An approach to surface EMG decomposition based on higher-order cumulants

Computer Methods and Programs in Biomedicine
Time-frequency characterization of cerebral hemodynamics of migraine sufferers as assessed by NIRS signals

EURASIP Journal on Advances in Signal Processing - Special issue on applications of time-frequency signal processing in wireless communications and bioengineering
Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of the heart rate variability signal

Computer Methods and Programs in Biomedicine
Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients

Computer Methods and Programs in Biomedicine
Estimating insulin sensitivity from glucose levels only: Use of a non-linear mixed effects approach and maximum a posteriori (MAP) estimation

Computer Methods and Programs in Biomedicine
Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal

Computer Methods and Programs in Biomedicine
Characterisation of linear predictability and non-stationarity of subcutaneous glucose profiles

Computer Methods and Programs in Biomedicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.