Path length entropy analysis of diastolic heart sounds

Authors:
Benjamin Griffel;Mohammad K. Zia;Vladamir Fridman;Cesare Saponieri;John L. Semmlow
Affiliations:
-;-;-;-;-
Venue:
Computers in Biology and Medicine
Year:
2013

Citing 3
Cited 0

Wavelet applications in medicine

IEEE Spectrum
Approximate statistical tests for comparing supervised classification learning algorithms

Neural Computation
Nonlinear Time Series Analysis

Nonlinear Time Series Analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

Early detection of coronary artery disease (CAD) using the acoustic approach, a noninvasive and cost-effective method, would greatly improve the outcome of CAD patients. To detect CAD, we analyze diastolic sounds for possible CAD murmurs. We observed diastolic sounds to exhibit 1/f structure and developed a new method, path length entropy (PLE) and a scaled version (SPLE), to characterize this structure to improve CAD detection. We compare SPLE results to Hurst exponent, Sample entropy and Multiscale entropy for distinguishing between normal and CAD patients. SPLE achieved a sensitivity-specificity of 80%-81%, the best of the tested methods. However, PLE and SPLE are not sufficient to prove nonlinearity, and evaluation using surrogate data suggests that our cardiovascular sound recordings do not contain significant nonlinear properties.