In:
Journal of Mechanics in Medicine and Biology, World Scientific Pub Co Pte Ltd, Vol. 18, No. 04 ( 2018-06), p. 1850039-
Abstract:
This paper aims to develop an algorithm to detect heart diseases through ordinary smartphones without additional equipment for cost accessibility. Among various vital signs emitted by organs, sounds can be easily observed and carry ample information. However, these sounds are small and noisy. Detecting anomalies involves great challenges in signal processing. This study presents a novel method that overcomes noises to estimate cardiovascular health. We use time-scale techniques in time series analysis to extract disease traits and suppress excessive ambient noises. Using datasets from PhysioNet, our model achieved a nearly 100% accuracy in heart disease diagnosis. Our approach also performs well under excessive noises for diseases producing heart murmurs. With heavy noise contaminated signals, training accuracy still closed to 100%, and the testing accuracy still remained around 84%.
Type of Medium:
Online Resource
ISSN:
0219-5194
,
1793-6810
DOI:
10.1142/S0219519418500392
Language:
English
Publisher:
World Scientific Pub Co Pte Ltd
Publication Date:
2018
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