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Reliable ECG analysis using recognition scores from multiple deep neural networks

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Abstract

The adoption of contactless patient monitoring has surged in response to the COVID-19 pandemic. After implementing remote electrocardiogram monitors, systematic management techniques are necessary to analyze extensive data and identify measurement errors. During the electrocardiogram measurement process, motion artifacts that may occur are often challenging to distinguish from the electrocardiogram itself because they share similar characteristics in terms of amplitude and frequency. To address these challenges, six deep neural networks capable of recognizing the features of normal electrocardiograms were developed. An algorithm that determines parameters representing the characteristics of electrocardiograms and identifies abnormal waveforms was created by combining the results of six deep neural networks. Waveforms from 10 patients were analyzed, and the differences between leads and individuals were quantified. Additionally, a recognition score was introduced to distinguish highly reliable electrocardiogram parameters while filtering out unreliable values caused by motion artifacts and measurement errors.

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Abbreviations

RS :

Recognition score

Ps :

P wave start point

Pe :

P wave endpoint

Qs :

QRS complex start point

Se :

QRS complex end point

Ts :

T wave start point

Te :

T wave end point

ECG :

Electrocardiogram

HR :

Heart rate

HRV :

Heart rate variability

V QRS,peak-to-peak :

Voltage change of QRS complex

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Acknowledgments

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (2020R1F1A 1073478). This research was supported by the Regional Innovation Strategy (RIS)” of the National Research Foundation of Korea (NRF), funded by the Ministry of Education (MOE) (2022RIS-005). This work was supported by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (Project Number: 1711138291, RS-2020-KD00014822182102130202). This research was supported by 2023 Regional Industry-linked University Open-Lab Development Support Program through the Commercializations Promotion Agency for R&D Outcomes (COMPA) funded by Ministry of Science and ICT (Research No. 1711202074).

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Authors and Affiliations

  1. Interdisciplinary Program in Biohealth-Machinery Convergence Engineering, Kangwon National University, Chuncheon-si, Gangwon-do, Korea

    Ji Woon Kim & Seong-Wook Choi

  2. School of Medicine, Kangwon National University, Chuncheon-si, Gangwon-do, Korea

    Sung Min Park

  3. Department of Mechanical & Biomedical Engineering, Kangwon National University, Chuncheon-si, Gangwon-do, Korea

    Seong-Wook Choi

Authors
  1. Ji Woon Kim
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  2. Sung Min Park
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  3. Seong-Wook Choi
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Corresponding author

Correspondence to Seong-Wook Choi.

Additional information

Ji Woon Kim has been serving as a Senior Researcher at NewM Co., Ltd., in Chuncheon-si, Republic of Korea, since 2021. He received his B.S. degree in biomedical engineering from the School of Mechanical and Biomedical Engineering at Kangwon National University, Republic of Korea, in 2019. He also obtained his master’s degree from the same institution in 2021. His main research interest is vital-signal analysis.

Sung Min Park has been affiliated with the Department of Thoracic and Cardiovascular Surgery in Chuncheon, Republic of Korea, where he currently holds the position of professor, since 2009. He received his B.S. degree in medicine from the school of medicine, Korea University, Rep. of Korea, in 1996, and his M.S. and Ph.D. degrees of medicine in the School of Medicine, Korea University, Rep. of Korea, in 1999 and 2005, respectively. He had worked for Korea University Hospital from 2004 to 2005, and for KyungHee University Medical Center from 2005 to 2009. His main research interests are medical image analysis and cardiovascular devices.

Seong-Wook Choi has been a Professor at the Department of Mechanical and Biomedical Engineering, Kangwon National University, Chuncheon, Republic of Korea, since 2007. He received his B.S. degree in biomedical electronics engineering from the School of Health Science, Yonsei University, Rep. of Korea, in 1997, and his M.S. and Ph.D. degrees in biomedical engineering from Seoul National University, Seoul, Korea, in 2001 and 2006, respectively. His main research interests are vital-signal analysis, biomedical instruments, and artificial organ.

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Kim, J.W., Park, S.M. & Choi, SW. Reliable ECG analysis using recognition scores from multiple deep neural networks. J Mech Sci Technol 38, 2169–2178 (2024). https://doi.org/10.1007/s12206-024-0345-0

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  • DOI: https://doi.org/10.1007/s12206-024-0345-0

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