Mohanachandran Nair Sindhu Swapna (Author), S. Sreejyoth (Author), A. Renjini (Author), Vimal Raj (Author), Sankaranarayana Iyer Sankararaman (Author)

Abstract

The manuscript elucidates the potential of phase portrait, fast Fourier transform, wavelet, and time-series analyses of the heart murmur (HM) of normal (healthy) and mitral regurgitation (MR) in the diagnosis of valve-related cardiovascular diseases. The temporal evolution study of phase portrait and the entropy analyses of HM unveil the valve dysfunctioninduced haemodynamics. A tenfold increase in sample entropy in MR from that of normal indicates the valve dysfunction. The occurrence of a large number of frequency components between lub and dub in MR, compared to the normal, is substantiated through the spectral analyses. The machine learning techniques, K-nearest neighbour, support vector machine, and principal component analyses give 100% predictive accuracy. Thus, the study suggests a surrogate method of auscultation of HM that can be employed cost-effectively in rural health centres.

Keywords

phase portrait;auscultation;mitral valve dysfunction;heart murmur;nonlinear time series analysis;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UNG - University of Nova Gorica
UDC: 53
COBISS: 112992771 Link will open in a new window
ISSN: 2190-5444
Views: 503
Downloads: 0
Average score: 0 (0 votes)
Metadata: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Other data

URN: URN:SI:UNG
Type (COBISS): Not categorized
Pages: str. 1-15
Volume: ǂVol. ǂ136
Issue: ǂiss. ǂ2
Chronology: 2021
DOI: 10.1140/epjp/s13360-021-01185-6
ID: 15766872