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A Novel Model for Screening Aortic Stenosis Using Phonocardiogram

  • Arash Gharehbaghi
  • Per Ask
  • Maria Lindén
  • Ankica Babic
Part of the IFMBE Proceedings book series (IFMBE, volume 48)

Abstract

This study presents an algorithm for screening aortic stenosis, based on heart sound signal processing. It benefits from an artificial intelligent-based (AI-based) model using a multi-layer perceptron neural network. The AI-based model learns disease related murmurs using non-stationary features of the murmurs. Performance of the model is statistically evaluated using two different databases, one of children and the other of elderly volunteers with normal heart condition and aortic stenosis. Results showed a 95% confidence interval of the high accuracy/sensitivity (84.1%-86.0%)/(86.0%-88.4%) thus exhibiting a superior performance to a cardiologist who relies on the conventional auscultation. The study suggests including the heart sound signal in the clinical decision making due to its potential to improve the screening accuracy.

Keywords

Aortic stenosis phonocardiography heart sound heart murmurs 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Arash Gharehbaghi
    • 1
    • 2
  • Per Ask
    • 1
  • Maria Lindén
    • 2
  • Ankica Babic
    • 1
    • 3
  1. 1.Physiological Measurements, Department of Biomedical EngineeringLinköping UniversityLinköpingSweden
  2. 2.Division of Intelligent Future Technologies, Biomedical Engineering GroupMälardalen UniversityVästeråsSweden
  3. 3.Department of Information Science and Media StudiesUniversity of BergenBergenNorway

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