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Mobile Phonocardiogram Diagnosis in Newborns Using Support Vector Machine.

Amir Mohammad Amiri1,2, Mohammadreza Abtahi3, Nick Constant4

  • 1Department of Physical Therapy, College of Public Health, Temple University, Philadelphia, PA 19140, USA. amir.amiri@temple.edu.

Healthcare (Basel, Switzerland)
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This study introduces an innovative phonocardiogram (PCG) system for newborn cardiac monitoring. The novel approach accurately classifies heart sounds using machine learning, enabling remote diagnosis of heart conditions.

Keywords:
SVMm-healthphonocardiogram

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Area of Science:

  • Biomedical Engineering
  • Cardiology
  • Signal Processing

Background:

  • Newborn phonocardiogram (PCG) monitoring is crucial yet challenging for early cardiac assessment.
  • Existing methods face limitations in noise reduction, data management, and remote accessibility.
  • Home-based monitoring and intelligent diagnosis systems are needed for improved neonatal cardiac care.

Purpose of the Study:

  • To develop a novel, intelligent system for cardiac monitoring using phonocardiogram (PCG) data in newborns.
  • To enhance the accuracy and accessibility of heart sound analysis through advanced signal processing and machine learning.
  • To enable remote diagnosis of congenital heart diseases using mobile devices and cloud-based analysis.

Main Methods:

  • Collected PCG data from multiple subjects over one year using an electronic stethoscope connected to a mobile device.
  • Implemented a system incorporating denoising, segmentation, cardiac cycle selection, and feature extraction.
  • Utilized various classifiers, including Support Vector Machine (SVM), for distinguishing healthy from pathological heart sounds.

Main Results:

  • The proposed system achieved 92.2% accuracy and an Area Under the Curve (AUC) of 0.98 in classifying heart sounds.
  • Support Vector Machine (SVM) demonstrated superior performance among the tested classifiers.
  • The training time for the SVM classifier was 1.14 seconds on a dataset of 116 samples.

Conclusions:

  • The developed PCG monitoring system offers a promising solution for accurate and accessible neonatal cardiac assessment.
  • The integration of denoising, segmentation, and machine learning enables efficient and reliable heart sound classification.
  • This technology facilitates remote monitoring and early diagnosis of pediatric heart conditions, potentially improving patient outcomes.