Title
An efficient data mining framework for the characterization of symptomatic and asymptomatic carotid plaque using bidimensional empirical mode decomposition technique.
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. Graphical abstract Outline for our efficient data mining framework for the characterization of symptomatic and asymptomatic carotid plaques.
Year
DOI
Venue
2018
10.1007/s11517-018-1792-5
Med. Biol. Engineering and Computing
Keywords
Field
DocType
Atherosclerosis,Carotid plaque,Neighborhood preserving,BEMD,SVM
Asymptomatic,Data mining,Artery walls,Blood flow,Carotid arteries,Stroke,Mathematics,Hilbert–Huang transform,Ultrasound
Journal
Volume
Issue
ISSN
56
9
0140-0118
Citations 
PageRank 
References 
1
0.35
27
Authors
5
Name
Order
Citations
PageRank
Filippo Molinari147235.76
U. Raghavendra21138.06
Anjan Gudigar3525.72
Kristen M Meiburger4485.08
Rajendra Acharya U54666296.34