Abstract:
ARTSENS is being developed as a fully automated
ultrasound based imageless system to facilitate mass screening
of patients for early detection of atherosclerosis especially in
low- and middle- income countries. ARTSENS uses a single
element ultrasound transducer and thus makes its
measurement on basis of observations on A-line. Positioning
the single element transducer on the carotid artery and
automatic identification of proximal and distal walls are a
major challenge in this device. In this paper, we explore
various machine learning methods namely – logistic
regression, support vector machine and Adaboost, on
selectively extracted features. The algorithms were trained on
data from 60 subjects and tested on data from 40 subjects.
Adaboost algorithm performed the best among the three
logging a 91.66% accuracy.
