An efficient data mining framework for the characterization of symptomatic and asymptomatic carotid plaque using bidimensional empirical mode decomposition technique
1.Department of Electronics and Telecommunications,Politecnico di Torino,Turin,Italy;2.Department of Instrumentation and Control Engineering, Manipal Institute of Technology,Manipal Academy of Higher Education,Manipal,India;3.Department of Electronics and Computer Engineering,Ngee Ann Polytechnic,Singapore,Singapore;4.Department of Biomedical Engineering, School of Science and Technology,SIM University,Singapore,Singapore;5.Department of Biomedical Engineering, Faculty of Engineering,University of Malaya,Kuala Lumpur,Malaysia
Abstract:
Atherosclerosis is a type of cardiovascular disease which may cause stroke. It is due to the deposition of fatty plaque in the artery walls resulting in the reduction of elasticity gradually and hence restricting the blood flow to the heart. Hence, an early prediction of carotid plaque deposition is important, as it can save lives. This paper proposes a novel data mining framework for the assessment of atherosclerosis in its early stage using ultrasound images. In this work, we are using 1353 symptomatic and 420 asymptomatic carotid plaque ultrasound images. Our proposed method classifies the symptomatic and asymptomatic carotid plaques using bidimensional empirical mode decomposition (BEMD) and entropy features. The unbalanced data samples are compensated using adaptive synthetic sampling (ADASYN), and the developed method yielded a promising accuracy of 91.43%, sensitivity of 97.26%, and specificity of 83.22% using fourteen features. Hence, the proposed method can be used as an assisting tool during the regular screening of carotid arteries in hospitals.
