DOI QR코드

DOI QR Code

개인별 맞춤형 심장질환 건강상태 모니터링 웹 서비스 개발

Development of Personalized Heart Disease Health Status Monitoring Web Service

  • 조영복 (국립안동대학교 컴퓨터교육과)
  • Young-bok Cho (Department of Computer Education, Andong National University)
  • 투고 : 2024.05.17
  • 심사 : 2024.06.11
  • 발행 : 2024.08.31

초록

최근 5년간 10대와 20대에서 부정맥 심장 질환 환자의 비율이 증가하고 있다. 심장 질환이 우리나라 사망원인 2위를 꾸준히 유지하며 수가 증가함에 따라 부정맥을 통한 심전도 검사가 중요해졌으나 심전도 전문 의료기기의 경우 경제적으로 부담이 되고, 큰 부피와 작동이 어려워 개인별 소장이 힘들다는 문제점으로 병원 방문을 통해 검사가 진행된다. 따라서 본 연구에서는 AD8232 센서를 활용하여 데이터를 측정하고, 실시간 모니터링을 통해 생체신호의 변화를 파악할 수 있도록 제공한다. 또한 개인의 민감정보 보호를 위해 세션 및 사용자 인증을 SSL기반으로 개인정보를 보호할 수 있는 개인 맞춤형 웹 서비스를 개발하였다.

Over the past five years, the proportion of patients with arrhythmia heart disease among teenagers and those in their 20s has been increasing. Heart disease has consistently remained the second leading cause of death in Korea and as the number has increased, electrocardiogram testing for arrhythmia has become important. However, specialized electrocardiogram medical devices are economically burdensome and are difficult to store individually in hospitals due to their large size and difficulty in operation. Testing is conducted through visits. Therefore, it is essential to enable individuals to perform ECG self-examinations using an Arduino-based ECG sensor that is affordable and easy to use in daily life, so that arrhythmia can be identified through individual ECG measurement. In this study, data is measured using an electrocardiogram sensor (AD8232), and changes in bio signals are visually provided through real-time monitoring, allowing users to make intuitive decisions and at the same time understand test results. To safeguard sensitive personal information, we have developed a web service that provides individual heart disease and customized health guides that can protect personal information through web vulnerability security using session and user authentication and SSL.

키워드

참고문헌

  1. Statistics Korea: 2022 cause of death statistics results [Internet]. Available: https://kostat.go.kr/board.es?mid=a10301060200&bid=218&act=view&list_no=427216.
  2. Korea Disease Control and Prevention Agency: Chronic disease prevention and management [Internet]. Available: https://www.kdca.go.kr/contents.es?mid=a20303020300.
  3. Health Insurance Review and Assessment Service [Internet]. Available: https://www.hira.or.kr/co/search.do?categoryFlag=n&checkSearchFields=ALL&collection=news&cookieonoff=on&period=A&query=%EA%B2%BD%ED%94%BC&realQuery=%EC%A1%B0%EA%B8%B0%EC%8B%9C%ED%96%89%EA%B0%80%EB%8A%A5&sort=DESC&startCount=0&tapMoveCheck=0.
  4. Yonhap News Agency, "The number of people with heart disease has increased by 20% in four years. Those in their 20s surged by a whopping 33%," [Online]. Available: https://www.yna.co.kr/view/AKR20231107134500530.
  5. S. H. Kim, "Management of common arrhythmia in the neurological intensive care unit," Journal of Neurocritical Care, vol. 11, no. 1, pp. 7-12, May 2018.
  6. G. Heo, M. H. Jung, and D. Ryu, "Implementation of an arduino-compatible board using ATmega128," Journal of the Korea Institute of Information and Communication Engineering, vol. 25, no. 10 , pp. 1441-1447, July 2021.
  7. J. H. Kim, "A study on real-time monitoring for moisture measurement of organic samples inside a drying oven using arduino based on open-source," Journal of Venture Innovation, vol. 5, no. 2, pp. 85-99, May 2022.
  8. A. Apicella, P. Arpaia, G. Mastrati, and N. Moccaldi, "EEG-based detection of emotional valence towards a reproducible measurement of emotions," Scientific Reports, vol. 11, no. 1, pp. 1-16, November 2021.
  9. J. W. Choi, O. Y. Kwon, J. H. Kwon. K. T. Oh, and S. U. Yoo, "Development of signal feature extraction system for ECG-Based heart disease classification," Journal of Multimedia Information Society, vol. 26, no. 1, pp. 75-83, January 2023.
  10. S. J. Lee, S. K. Kim, and T. K. Kim, "ECG compression and transmission based on template matching," Journal of Internet Computing and Services, vol. 23, no. 1, pp. 31-38, February 2022,
  11. J. H. Yu, "Effects of neuromuscular electrical stimulation on cardiorespiratory function in adults with obesity," Journal of Neurotherapy, vol. 24, no. 3, pp. 9-1, February 2020.
  12. F. Murat, O. Yildirim, M. Talo, U. B. Baloglu, Y. Demir, and U. R. Acharya, "Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review," Journal of Computers in Biology and Medicine, vol. 120, pp. 103725-103726, May 2020.