Annual Conference of KIPS (한국정보처리학회:학술대회논문집)

  • 2024.10a
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  • Pages.461-462
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  • 2024
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  • 2005-0011(pISSN)
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  • 2671-7298(eISSN)
Korea Information Processing Society (한국정보처리학회)
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Human Cardiac Abnormality Detection Using Deep Learning with Heart Sound in Newborn Children

  • Eashita Wazed (Dept. of Artificial Intelligence Convergence, Chonnam National University) ;
  • Hieyong Jeong (Dept. of Artificial Intelligence Convergence, Chonnam National University)
  • Published : 2024.10.31
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Abstract

In pediatric healthcare, early detection of cardiovascular diseases in newborns is crucial. Analyzing heart sounds using stethoscopes can be subjective and reliant on physician expertise, potentially leading to delayed diagnosis. There is a need for a simple method that can help even inexperienced doctors detect heart abnormalities without an electrocardiogram or ultrasound. Automated heart sound diagnosis systems can aid clinicians by providing accurate and early detection of abnormal heartbeats. To address this, we developed an intelligent deep-learning model incorporating CNN and LSTM to detect heart abnormalities based on artificial intelligence using heart sound data from stethoscope recordings. Our research achieved a high accuracy rate of 97.8%. Using audio data to introduce advanced models for cardiac abnormalities in children is essential for enhancing early detection and intervention in pediatric cardiovascular healthcare.

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Acknowledgement

This research was supported by the Basic Science Research Program through the National Research Foundation (NRF) of Korea grant, funded by the Ministry of Education (NRF-2021R1I1A3055210), and partially by Institute of Information & communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2023-RS-2023-00256629) grant funded by the Korea government(MSIT).

References

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