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Comparison of sample entropy and AR-models for heart sound-based detection of coronary artery disease

Authors
Publisher
I E E E Computer Society
Publication Date
Disciplines
  • Computer Science
  • Mathematics
  • Medicine

Abstract

Comparison of Sample Entropy and AR-models for Heart Sound-based Detection of Coronary Artery Disease Samuel E Schmidt1, John Hansen1, Claus Holst Hansen2, Egon Toft1, Johannes J Struijk1 1Department of Health Science and Technology, Aalborg University, Aalborg, Denmark 2Center for Cardiovascular Research, Aalborg Hospital, Århus University Hospitals, Denmark Abstract The first reported observations of rare diastolic murmurs in patients with coronary artery disease (CAD) date back to the late sixties. Subsequently several studies have the examined signal processing methods for identification of the weak murmurs. One such method is autoregressive (AR) models. A recent study showed that CAD changes the entropy of the diastolic sound. The aim of the current study is to analyze the relationship between features from an AR-model and features describing signal entropy. Sample entropy and the poles of AR models were calculated from diastolic intervals in heart sound recordings randomly selected from a database of stethoscope recordings of good quality. In total 100 recordings were analyzed (50 patients with two recordings from each). The recordings were band pass filtered with a 8 order Chebyshev filter with pass band edge frequency at 50 Hz and 500 Hz. The result shows that both measures equally separates the CAD patients from non-CAD patients, but the measures are strongly correlated. 1. Introduction Coronary artery disease is the top single cause of death in the western world. But established diagnostic methods, such as coronary angiography and exercise tests, are costly, time consuming and they are a burden for the patients. Previous studies have shown that heart sounds may contain weak murmurs caused by turbulence in poststenotic blood flow in the coronary arteries and that this turbulence related sound is a potential diagnostic indicator of CAD [1]. The murmurs are rarely audible, but several signal processing algorithms to automatically

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