ABSTRACT
Blood pressure is one vital sign that can predict and detect hypertensive on individuals. One prevalent problem in developing Asian countries is hypertension; and it has been a global health problem. The main objective of this study is to determine the BP of the patient by using Arduino-Based Sensors: SEN0213 Heart Rate Sensor and SEN0203 PPG Pulse Sensor using Pulse Transit Time (PTT). Linear and 4th Order Polynomial Regression is used to learn the equations to calculate the Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) of the patients based on the Pulse Transit Time top and bottom (PTTt and PTTb) with DBP and SBP measured by a medical expert using clinical equipment. The researchers examined 30 individuals in order to incorporate equations in relation to SBP and DBP using PTT. P-values of the coefficients were less than 0.05, which means that the data entered for regression analysis were not by chance and that the coefficients were significant. A 84.06-84.61% and 67-71% of SBP and DBP data, respectively fit the model in an observation of 30. A 0.9199 and 0.8458 multiple R value for SBP and DBP is near 1, which means that both datasets have a nearly-perfect positive-relationship.
- World Health Organization, "Global status report on noncommunicable diseases 2014." Geneva, Switzerland: World Health Organization, 2014.Google Scholar
- World Health Organization, "Controlling high blood pressure," World Health Organization, 03-Mar-2016.Google Scholar
- A. A. Jambora, "Hypertension on the rise in kids due to high-salt diet, frequent gadget use," Inquirer Lifestyle Ang Kiukok From Xiamen to Davao to National Artist Comments, 21-Mar-2017.Google Scholar
- E. Agron, "Prevalence of hypertension among Filipinos increasing - PSH," PCHRD, 13-Jun-2012.Google Scholar
- J. M. N. Narvaez, and G. O. Avendaño, "Design and Implementation of Vital Signs Simulator for Patient Monitor," Journal of Telecommunication, Electronic and Computer Engineering, vol. 9, no. 2-4, pp. 7--9, 2017.Google Scholar
- G. Janjua, D. Guldenring, D. Finlay, and J. Mclaughlin, "Wireless chest wearable vital sign monitoring platform for hypertension," 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017.Google Scholar
- W.-H. Lin, H. Wang, O. W. Samuel, and G. Li, "Using a new PPG indicator to increase the accuracy of PTT-based continuous cuffless blood pressure estimation," 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017.Google Scholar
- W. Zhi-Hao, D. Kurniadi, K. Yu-Fan, Hendrick, J. Gwo-Jia, and H. Gwo-Jiun, "Wireless network home health care system of social welfare institution," 2017 5th International Conference on Electrical, Electronics and Information Engineering (ICEEIE), 2017.Google Scholar
- S. Sali and C. S. Parvathi, "Integrated wireless instrument for heart rate and body temperature measurement," 2017 2nd International Conference for Convergence in Technology (I2CT), pp. 457--463, 2017.Google Scholar
- M. A. Al-Shaher and N. J. Al-Khafaji, "E-healthcare system to monitor vital signs," 2017 9th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 2017.Google Scholar
- P. R. Vittal and N. Sriraam, "A pilot study on patch sensor-based Photo Plethysmography (PPG) for heart rate measurements," 2016 International Conference on Circuits, Controls, Communications and Computing (I4C), 2016.Google Scholar
- V. R. R. Samson, U. B. Sai, P L S D Malleswara Rao, K. K. Eswar, and S. P. Kumar, "Automatic oxygen level control of patient using fuzzy logic and arduino," 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC), 2017.Google Scholar
- Y.-H. Kao, P. C.-P. Chao, T.-Y. Tu, K.-Y. Chiang, and C.-L. Wey, "A new cuffless optical sensor for blood pressure measuring with self-adaptive signal processing," 2016 IEEE Sensors, 2016.Google Scholar
- R. N. Kirtana and Y. V. Lokeswari, "An IoT based remote HRV monitoring system for hypertensive patients," 2017 International Conference on Computer, Communication and Signal Processing (ICCCSP), 2017.Google Scholar
- W. D. Alwis, R. Rajapaksha, N. Ranaweera, P. Pitigalaarachchi, and A. Pasqual, "Parametric model between Pulse Transit Time and Systolic and Diastolic Blood Pressures for non-invasive Blood Pressure estimation," 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES), 2014.Google Scholar
- Centers for Disease Control and Prevention, "Measuring Blood Pressure" Centers for Disease Control and Prevention, 17-Oct-2017.Google Scholar
- Badnjević Almir, M. Cifrek, Magjarević Ratko, and Džemić Zijad, Inspection of Medical Devices: For Regulatory Purposes. Singapore: Springer Singapore, 2018.Google Scholar
- K. Jade, "13 Cardinal Rules for Getting Accurate Blood Pressure Readings at Home," University Health News, 20-Jul-2017.Google Scholar
- L. Grajales and I. Nicolaescu, "Wearable Multisensor Heart Rate Monitor," International Workshop on Wearable and Implantable Body Sensor Networks (BSN06), pp. 17--21, Jul. 2017. Google ScholarDigital Library
- S. Yun, C.-S. Son, S.-H. Lee, and W.-S. Kang, "Forecasting of heart rate variability using wrist-worn heart rate monitor based on hidden Markov model," 2018 International Conference on Electronics, Information, and Communication (ICEIC), pp. 107--108, May 2018.Google Scholar
- D. C. Montgomery and G. C. Runger, Applied statistics and probability for engineers, 6th ed. Hoboken, NJ: WileGoogle Scholar
Index Terms
- Vital Signs Determination from ECG and PPG Signals Obtained from Arduino Based Sensors
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