Comparison of Machine Learning Algorithms Using Manual/Automated Features on 12-Lead Signal Electrocardiogram Classification: A Large Cohort Study on Students Aged Between 6 to 18 Years Old.

Journal: Cardiovascular engineering and technology
Published Date: Oct 17, 2023

Abstract

PROPOSE: An electrocardiogram (ECG) has been extensively used to detect rhythm disturbances. We sought to determine the accuracy of different machine learning in distinguishing abnormal ECGs from normal ones in children who were examined using a resting 12-Lead ECG machine, and we also compared the manual and automated measurement using the modular ECG Analysis System (MEANS) algorithm of ECG features.

Authors

  • Ghasem Hajianfar
    Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran.
  • Mohammadrafie Khorgami
    Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Niayesh Highway, Valiasr Ave., Tehran, 19969111541, Iran. rafikhorgami@gmail.com.
  • Yousef Rezaei
    Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Niayesh Highway, Valiasr Ave., Tehran, 19969111541, Iran.
  • Mehdi Amini
    From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital.
  • Niloufar Samiei
    Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Niayesh Highway, Valiasr Ave., Tehran, 19969111541, Iran.
  • Avisa Tabib
    Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Niayesh Highway, Valiasr Ave., Tehran, 19969111541, Iran.
  • Bahareh Kazem Borji
    Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Niayesh Highway, Valiasr Ave., Tehran, 19969111541, Iran.
  • Samira Kalayinia
    Cardiogenetic Research Center, Medical and Research Center, Rajaie Cardiovascular, University of Medical Sciences, Tehran, Iran. samira.kalayi@yahoo.com.
  • Isaac Shiri
    Biomedical and Health Informatics, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.
  • Saeid Hosseini
    School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran; ST Electronics - SUTD Cyber Security Laboratory, Singapore University of Technology and Design, Singapore. Electronic address: saeid.hosseini@uq.net.au.
  • Mehrdad Oveisi
    Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran; Department of Computer Science, University of British ColumbiaVancouver, BC V6T 1Z4, Canada.

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