Neural Computing and Applications

, Volume 28, Issue 10, pp 2869–2878 | Cite as

Automated screening of congestive heart failure using variational mode decomposition and texture features extracted from ultrasound images

  • U. RaghavendraEmail author
  • U. Rajendra Acharya
  • Anjan Gudigar
  • Ranjan Shetty
  • N. Krishnananda
  • Umesh Pai
  • Jyothi Samanth
  • Chaithra Nayak
New Trends in data pre-processing methods for signal and image classification


Heart is an important and hardest working muscular organ of the human body. Inability of the heart to restore normal perfusion to the entire body refers to cardiac failure, which then with symptoms results in manifestation of congestive heart failure (CHF). Impairment in systolic function associated with chronic dilation of left ventricle is referred as dilated cardiomyopathy (DCM). The clinical examination, surface electrocardiogram (ECG), chest X-ray, blood markers and echocardiography play major role in the diagnosis of CHF. Though the ECG manifests chamber enlargement changes, it does not possess sensitive marker for the diagnosis of DCM, whereas echocardiographic assessment can effectively reveal the presence of asymptomatic DCM. This work proposes an automated screening method for classifying normal and CHF echocardiographic images affected due to DCM using variational mode decomposition technique. The texture features are extracted from variational mode decomposed image. These features are selected using particle swarm optimization and classified using support vector machine classifier with different kernel functions. We have validated our experiment using 300 four-chamber echocardiography images (150: normal, 150: CHF) obtained from 50 normal and 50 CHF patients. Our proposed approach yielded maximum average accuracy, sensitivity and specificity of 99.33%, 98.66% and 100%, respectively, using ten features. Thus, the developed diagnosis system can effectively detect CHF in its early stage using ultrasound images and aid the clinicians in their diagnosis.


Congestive heart failure Dilated cardiomyopathy Machine learning Texture features VMD 


Compliance with ethical standards

Conflict of interest

None of the authors have any conflict of interest.


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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  • U. Raghavendra
    • 1
    Email author
  • U. Rajendra Acharya
    • 2
    • 3
    • 4
  • Anjan Gudigar
    • 1
  • Ranjan Shetty
    • 5
  • N. Krishnananda
    • 6
  • Umesh Pai
    • 6
  • Jyothi Samanth
    • 6
  • Chaithra Nayak
    • 6
  1. 1.Department of Instrumentation and Control Engineering, Manipal Institute of TechnologyManipal UniversityManipalIndia
  2. 2.Department of Electronics and Computer EngineeringNgee Ann PolytechnicSingaporeSingapore
  3. 3.Department of Biomedical Engineering, School of Science and TechnologySIM UniversitySingaporeSingapore
  4. 4.Department of Biomedical Engineering, Faculty of EngineeringUniversity of MalayaKuala LumpurMalaysia
  5. 5.Department of Cardiology, Kasturba Medical College and HospitalManipal UniversityManipalIndia
  6. 6.Department of Cardiovascular Technology, School of Allied Health SciencesManipal UniversityManipalIndia

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