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Print ISSN: 1582-7445
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doi: 10.4316/AECE


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  1/2021 - 5

A Semi-automatic Heart Sounds Identification Model and Its Implementation in Internet of Things Devices

JUSAK, J. See more information about JUSAK, J. on SCOPUS See more information about JUSAK, J. on IEEExplore See more information about JUSAK, J. on Web of Science, PUSPASARI, I. See more information about  PUSPASARI, I. on SCOPUS See more information about  PUSPASARI, I. on SCOPUS See more information about PUSPASARI, I. on Web of Science, KUSUMAWATI, W. I. See more information about KUSUMAWATI, W. I. on SCOPUS See more information about KUSUMAWATI, W. I. on SCOPUS See more information about KUSUMAWATI, W. I. on Web of Science
 
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Download PDF pdficon (3,956 KB) | Citation | Downloads: 194 | Views: 221

Author keywords
Internet of Things, phonocardiography, signal detection, system identification, telemedicine

References keywords
heart(16), signal(8), sounds(7), healthcare(6), access(6), time(5), system(5), sound(5), processing(5), security(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2021-02-28
Volume 21, Issue 1, Year 2021, On page(s): 45 - 56
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2021.01005
Web of Science Accession Number: 000624018800005
SCOPUS ID: 85106421249

Abstract
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Identification of heart sound signals in the form of a phonocardiogram (PCG) has recently attracted the attention of many researchers along with the development of small devices and global Internet connection in a way to offer automatic illness detection and monitoring. In this work, we propose a semi-automatic envelope-based heart sounds identification method called the Largest Interval Heart Sounds Detection (LiHSD) that exploits the superiority of the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and the cubic spline interpolation to discover several heart sounds' components such as period and location of S1 and S2, an interval of a cardiac cycle, and to obtain the duration and location of murmurs. Evaluation of the proposed system over several life sample data showed promising results comparable to the previous models. The algorithm was able to capture the largest interval of S1 and S2. The examination for normal heart sounds exhibited detection accuracy 98 percent, whereas for anomaly heart sounds samples the detection accuracy ranging from 89 percent to 97.5 percent. Furthermore, the proposed system has been successfully implemented in a real Internet of Things device while eyeing its contribution to the future of the smart healthcare system.


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References Weight

Web of Science® Citations for all references: 13,433 TCR
SCOPUS® Citations for all references: 18,688 TCR

Web of Science® Average Citations per reference: 407 ACR
SCOPUS® Average Citations per reference: 566 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-07-22 01:54 in 186 seconds.




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