mca 16(10): e1

Research Article

PhonoSys: Mobile Phonocardiography Diagnostic System for Newborns

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  • @ARTICLE{10.4108/eai.14-10-2015.2261614,
    author={Amir Mohammad Amiri and Giuliano Armano and Amir Mohammad Rahmani and Kunal Mankodiya},
    title={PhonoSys: Mobile Phonocardiography Diagnostic System for Newborns},
    journal={EAI Endorsed Transactions on Mobile Communications and Applications},
    volume={3},
    number={10},
    publisher={ACM},
    journal_a={MCA},
    year={2017},
    month={6},
    keywords={phonocardiogram, heart murmurs, m-health, random forest, newborn},
    doi={10.4108/eai.14-10-2015.2261614}
}
  • Amir Mohammad Amiri
    Giuliano Armano
    Amir Mohammad Rahmani
    Kunal Mankodiya
    Year: 2017
    PhonoSys: Mobile Phonocardiography Diagnostic System for Newborns
    MCA
    EAI
    DOI: 10.4108/eai.14-10-2015.2261614
Amir Mohammad Amiri1, Giuliano Armano2, Amir Mohammad Rahmani3, Kunal Mankodiya4,*
  • 1: University of Rhode Island, RI, USA
  • 2: University of Cagliari
  • 3: University of Turku
  • 4: University of Rhode Island
*Contact email: kunalm@ele.uri.edu

Abstract

Heart murmurs have been found to be a life threatening condition for the newborns who are born with cardiac abnormalities. The first sign of pathological changes of heart valves appears in phonocardiogram which contains very useful information about cardiovascular system. It is a challenging venture to distinguish pathological murmurs from innocent ones. In this paper we have developed a diagnostic algorithm called PhonoSys to analyze PCG using random forest. PhonoSys algorithm will run on mobile devices for remote PCG analysis. We recorded PCG signals from 120 newborns who are either healthy or with cardiac abnormalities. Eventually, in this study, 97.6% accuracy, 96.8% sensitivity, and 98.4% speci city were obtained to classify between innocent and pathological murmurs.

Keywords
phonocardiogram, heart murmurs, m-health, random forest, newborn
Published
2017-06-27
Publisher
ACM
http://dx.doi.org/10.4108/eai.14-10-2015.2261614

Copyright © 2015 K. Mankodiya et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.