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Implementation of PTT Change Monitoring System According to Exercise Intensity  

Lee, Ji-Su (Division of Computer Engineering, Dongseo University)
Kim, Dong-Chan (Division of Computer Engineering, Dongseo University)
Lee, Gyeong-Tack (Division of Computer Engineering, Dongseo University)
Kim, Gyeong-Seop (Division of Computer Engineering, Dongseo University)
Noh, Yun-Hong (Department of Computer Engineering, Busan Digital University)
Jeong, Do-Un (Division of Computer Engineering, Dongseo University)
Publication Information
Journal of the Institute of Convergence Signal Processing / v.21, no.1, 2020 , pp. 49-54 More about this Journal
Abstract
Cardiovascular disease is the leading cause of death worldwide and is caused by a variety of causes. The highest risk factor for cardiovascular disease is high blood pressure, which has no obvious symptoms, but if left untreated, it causes several complications. In order to treat hypertension, medication and regular exercise are required. In people with high blood pressure, excessive physical activity can put a great strain on the heart and lead to cardiovascular disease. Therefore, there is a need for an exercise intensity monitoring system through PTT measurement that can perform exercise at an appropriate intensity. In this study, we implemented a PTT change monitoring system according to exercise intensity by calculating PTT through ECG and PPG measurement. The implemented system differentiates the R-peak of the ECG and P-peak of the PPG, and calculates the PTT using the time difference between R-peak and P-peak. A running experiment was conducted to monitoring PTT change according to exercise intensity. As a result of the experiment, low intensity PTT is 0.313s, moderate is 0.220s, high is 0.188s, it was confirmed that the PTT decreased as the exercise increase increased.
Keywords
Hypertension; Electrocardiogram; Photoplethysmography; Pulse transit time; Exercise intensity;
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  • Reference
1 WHO(World Health Organization), "The top 10 causes of death", 2018.
2 F. C. Bennis, C. van Pul, J. J. van den Bogaart, P. Andriessen, B. W. Kramer, T. Delhaas, "Artifacts in pulse transit time measurement using standard patient monitoring equipment", PloS one, vol. 14, no. 6, e0218784, 2019.
3 F. Miao, Z. D. Liu, J. K. Liu, B. Wen, Q. Y. He, Y. Li, "Multi-sensor fusion approach for cuff-less blood pressure measurement", IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 1, 79-91, 2019.
4 C. Yang, Y. Dong, Y. Chen, N. Tavassolian, "A smartphone-only pulse transit time monitor based on cardio-mechanical and photoplethysmography modalities", IEEE Transactions on Biomedical Circuits and Systems, vol. 13, no. 6, 1462-1470, 2019.
5 E. A. Massawe, K. Michael, S. Kaijage, P. Seshaiyer, "Design and Analysis of Smart Sensing System for Animal Emotions Recognition", International Journal of Computer Applications, 169(11), 46-50, 2017.
6 M. Ignaszewski, B. Lau, S. Wong, S. Isserow, "The science of exercise prescription: Martti Karvonen and his contributions", British Columbia Medical Journal, 59(1), 38-41, 2017.
7 M. G. Schultz, A. La Gerche, J. E. Sharman, "Blood pressure response to exercise and cardiovascular disease", Current Hypertension Reports, vol. 19, no. 11, 89, 2017.
8 I. Sharifi, S. Goudarzi, M. B. Khodabakhshi, "A novel dynamical approach in continuous cuffless blood pressure estimation based on ECG and PPG signals", Artificial Intelligence in Medicine, vol. 97, 143-151, 2019.