An efficient data mining framework for the characterization of symptomatic and asymptomatic carotid plaque using bidimensional empirical mode decomposition technique

Filippo Molinari, U. Raghavendra, Anjan Gudigar, Kristen M. Meiburger, U. Rajendra Acharya

Research output: Contribution to journalArticle

9 Citations (Scopus)

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. [Figure not available: see fulltext.]

Original languageEnglish
Pages (from-to)1-15
Number of pages15
JournalMedical and Biological Engineering and Computing
DOIs
Publication statusPublished - 23-02-2018

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Computer Science Applications

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