|
Cardiac Adipose Tissue (CAT) is a type of visceral fat that is deposited between the myocardium and pericardium. An increased volume of CAT has been recognized as a crucial contributor to cardiovascular and coronary artery diseases. This tissue is a metabolically active organ that affects the cardiac functioning by secreting inflammatory adipokines making it a hazard when present in excess amounts. Quantifying CAT, therefore, can be an important factor in understanding the level of cardiovascular risk. The study presented in this paper investigates the use of frequency content from echocardiography and spectral analysis techniques in differentiating three different cardiac tissue types, including the adipose tissue. Thirteen spectral parameters were computed from the power spectrum of the radio frequency data in three different bandwidth ranges, including 3, 6 and 20 dB. Autoregressive models of order 4 were used as they provide effective estimates of the power spectrum for short-time data. The derived spectral parameters were used in generating random forests for tissue classification. Out of the total 175 ROIs available, 70% of the data was divided into training data and the remaining used as test data. The random forest classifier with 50 classification trees resulted in an overall accuracy of 92.4%, sensitivity of 91.1%, specificity of 93.9%, and Youden’s index of 0.85 for a 20dB bandwidth. This result demonstrates the potential of echocardiography and spectral analysis techniques in differentiating CAT, myocardium, and blood.
|
