A preliminary study for the assessment of hypertension using static and dynamic IR thermograms.

Structural changes in blood vessels occur due to prolonged hypertension. Early detection of blood pressure (mm Hg) is essential for disease prevention. The aim of this work is to propose a computer-aided diagnostic (CADx) model for the diagnosis of hypertension using variables derived from non-contact static and dynamic thermal imaging in comparison with the pulse wave velocity (PWV)-derived parameters. Static and dynamic infrared (IR) thermograms of selected skin areas of the body from known hypertensive (n=14) and age- and sex-matched normal subjects were captured. The average skin surface temperature [SST (°C)] of selected skin areas of the body was calculated from a static IR thermogram. After denoising the dynamic IR thermogram using wavelets, the statistical variables power, mean, standard deviation (SD), variance, skewness and kurtosis were calculated. The variables derived from both static and dynamic thermograms were used to develop the CADx model. The performance of the CAD model was also tested by feature selection using principal component analysis (PCA). An accuracy of 75% (sensitivity=78.6%, specificity=71.4%) could be achieved with the average SST (°C) of the static IR thermogram alone. The statistical variables derived from the dynamic IR thermogram alone gave an accuracy of 82% (and 85% after feature selection by PCA), whereas the accuracy using standard methods like variables derived from PWV was only 71.4% (with and without feature selection). The highest accuracy of 89% could be achieved by combining variables like average SST (°C) measured from static and dynamic IR thermograms and PWV-derived variables.