Medical & Biological Engineering & Computing

, Volume 56, Issue 9, pp 1579–1593 | Cite as

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

  • Filippo MolinariEmail author
  • U. Raghavendra
  • Anjan Gudigar
  • Kristen M. Meiburger
  • U. Rajendra Acharya
Original Article


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


Atherosclerosis Carotid plaque Neighborhood preserving BEMD SVM 


Compliance with ethical standards

All the images were acquired after the subjects signed an informed consent about the treatment of their data. The use of the images was approved by the institutional review board of the Gradenigo Hospital.


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Copyright information

© International Federation for Medical and Biological Engineering 2018

Authors and Affiliations

  1. 1.Department of Electronics and TelecommunicationsPolitecnico di TorinoTurinItaly
  2. 2.Department of Instrumentation and Control Engineering, Manipal Institute of TechnologyManipal Academy of Higher EducationManipalIndia
  3. 3.Department of Electronics and Computer EngineeringNgee Ann PolytechnicSingaporeSingapore
  4. 4.Department of Biomedical Engineering, School of Science and TechnologySIM UniversitySingaporeSingapore
  5. 5.Department of Biomedical Engineering, Faculty of EngineeringUniversity of MalayaKuala LumpurMalaysia

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