The development of sensitive and low-cost techniques for identifying valve dysfunction has become inevitable in the context of increasing death due to cardiac diseases. The present work attempts to propose a novel technique for cardiac auscultation based on graph theory. The sixty heart sound signals from normal heart (NMH) and with aortic stenosis (ASH) are subjected to Fast Fourier Transform (FFT) and complex network analyses. The murmur signals, a time-series signal, carry information about the blood flow through the heart, which gets exposed in the graph constructed and its features. The finer details of the murmur signal from the defective aortic valve and the normal aortic valve are reflected as the increased number of frequency components in FFT and as interconnected clusters without uncorrelated nodes in the graph of ASH. The distinction in graph features forms the basis of classification based on machine learning techniques (MLTs). When the unsupervised MLT-principal component analysis gives 86.8% total variance, the supervised MLTs-K nearest neighbor (KNN), support vector machine, and KNN subspace ensemble classifiers give 100%, 95.6%, and 90.9% prediction accuracy, suggesting its potential in remote auscultation in rural health centers.
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Unwrapping aortic valve dysfunction through complex network analysis: A biophysics approach
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28 August 2022
Research Article|
August 30 2022
Unwrapping aortic valve dysfunction through complex network analysis: A biophysics approach
Special Collection:
Special Collection Recognizing Women in Applied Physics
Vijayan Vijesh;
Vijayan Vijesh
a)
(Formal analysis, Methodology, Software, Writing – original draft)
1
Department of Futures Studies, University of Kerala
, Trivandrum 695581, Kerala, India
a)Author to whom correspondence should be addressed: swapna.nair@ung.si
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Mohanachandran Nair Sindhu Swapna
;
Mohanachandran Nair Sindhu Swapna
a)
(Formal analysis, Investigation, Methodology, Software, Writing – original draft, Writing – review & editing)
2
Laboratory for Environmental and Life Sciences, University of Nova Gorica
, Vipavska 13, Nova Gorica SI-5000, Slovenia
3
Department of Optoelectronics, University of Kerala
, Trivandrum 695581, Kerala, India
a)Author to whom correspondence should be addressed: swapna.nair@ung.si
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Krishan Nair Satheesh Kumar;
Krishan Nair Satheesh Kumar
(Formal analysis, Investigation, Methodology, Software, Supervision, Writing – review & editing)
1
Department of Futures Studies, University of Kerala
, Trivandrum 695581, Kerala, India
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Sankaranarayana Iyer Sankararaman
Sankaranarayana Iyer Sankararaman
(Conceptualization, Investigation, Methodology, Software, Supervision, Writing – original draft, Writing – review & editing)
3
Department of Optoelectronics, University of Kerala
, Trivandrum 695581, Kerala, India
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a)Author to whom correspondence should be addressed: swapna.nair@ung.si
Note: This paper is part of the Special Collection Recognizing Women in Applied Physics.
J. Appl. Phys. 132, 084904 (2022)
Article history
Received:
June 07 2022
Accepted:
July 26 2022
Citation
Vijayan Vijesh, Mohanachandran Nair Sindhu Swapna, Krishan Nair Satheesh Kumar, Sankaranarayana Iyer Sankararaman; Unwrapping aortic valve dysfunction through complex network analysis: A biophysics approach. J. Appl. Phys. 28 August 2022; 132 (8): 084904. https://doi.org/10.1063/5.0102120
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