Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Acquisition and Classification of ECG Parameters with Multiple Deep Neural Networks

다중 심층신경망을 이용한 심전도 파라미터의 획득 및 분류

  • Ji Woon, Kim (Interdisciplinary Program in Biohealth-Machinery Convergence Engineering, Kangwon National University) ;
  • Sung Min, Park (Department of Thoracic & Cardiovascular Surgery, School of Medicine, Kangwon National University) ;
  • Seong Wook, Choi (Interdisciplinary Program in Biohealth-Machinery Convergence Engineering, Kangwon National University)
  • 김지운 (강원대학교 문화예술.공과대학 스마트헬스과학기술융합학과) ;
  • 박성민 (강원대학교 의학전문대학원 흉부외과) ;
  • 최성욱 (강원대학교 문화예술.공과대학 스마트헬스과학기술융합학과)
  • Received : 2022.11.29
  • Accepted : 2022.12.12
  • Published : 2022.12.31
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Abstract

As the proportion of non-contact telemedicine increases and the number of electrocardiogram (ECG) data measured using portable ECG monitors increases, the demand for automatic algorithms that can precisely analyze vast amounts of ECG is increasing. Since the P, QRS, and T waves of the ECG have different shapes depending on the location of electrodes or individual characteristics and often have similar frequency components or amplitudes, it is difficult to distinguish P, QRS and T waves and measure each parameter. In order to measure the widths, intervals and areas of P, QRS, and T waves, a new algorithm that recognizes the start and end points of each wave and automatically measures the time differences and amplitudes between each point is required. In this study, the start and end points of the P, QRS, and T waves were measured using six Deep Neural Networks (DNN) that recognize the start and end points of each wave. Then, by synthesizing the results of all DNNs, 12 parameters for ECG characteristics for each heartbeat were obtained. In the ECG waveform of 10 subjects provided by Physionet, 12 parameters were measured for each of 660 heartbeats, and the 12 parameters measured for each heartbeat well represented the characteristics of the ECG, so it was possible to distinguish them from other subjects' parameters. When the ECG data of 10 subjects were combined into one file and analyzed with the suggested algorithm, 10 types of ECG waveform were observed, and two types of ECG waveform were simultaneously observed in 5 subjects, however, it was not observed that one person had more than two types.

Keywords

Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2020R1F1A1073478). This research was supported by "Regional Innovation Strategy (RIS)" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2022RIS-005). This work was supported by the Commercializations Promotion Agency for R&D Outcomes (COMPA) grant funded by the Korean Government (Miinistery of Science and ICT) (2022)

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