An Extreme Learning Machine for Blood Pressure Waveform Estimation using the Photoplethysmography Signal
by Gonzalo Tapia 1 
, Rodrigo Salas 1,2,3,4,* 
, Matías Salinas 1,2,3 , Carolina Saavedra 1,2 , Alejandro Veloz 1,2, Alexis Arriola 1,2, Steren Chabert 1,2,4 , Antonio Glaría 1
1 Escuela de Ingeniería C. Biomédica, Universidad de Valparaíso, Valparaíso, Chile.
2 Centro de Investigación y Desarrollo en Ingeniería en Salud, CINGS-UV, Universidad de Valparaíso, Valparaíso, Chile.
3 Programa de Doctorado en Ciencias e Ingeniería para la Salud, Universidad de Valparaíso, Valparaíso, Chile.
4 Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile.
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 1, Issue 4, Page # 161-174, 2022; DOI: 10.55708/js0104018
Keywords: Extreme Learning Machines, Adaptive Estimation, Biomedical Measurement, Photoplethysmography, Noninvasive treatment, Medical Devices
Received: 07 March 2022, Revised: 05 April 2022, Accepted: 06 April 2022, Published Online: 23 April 2022
APA Style
Tapia, G., Salas, R., Salinas, M., Saavedra, C., Veloz, A., Arriola, A., Chabert, S., & Glaría, A. (2022, April). An Extreme Learning Machine for Blood Pressure Waveform Estimation using the Photoplethysmography Signal. Journal of Engineering Research and Sciences, 1(4), 161–174. https://doi.org/10.55708/js0104018
Chicago/Turabian Style
Tapia, Gonzalo, Rodrigo Salas, Matías Salinas, Carolina Saavedra, Alejandro Veloz, Alexis Arriola, Steren Chabert, and Antonio Glaría. “An Extreme Learning Machine for Blood Pressure Waveform Estimation Using the Photoplethysmography Signal.” Journal of Engineering Research and Sciences 1, no. 4 (April 2022): 161–74. https://doi.org/10.55708/js0104018.
IEEE Style
G. Tapia et al., “An Extreme Learning Machine for Blood Pressure Waveform Estimation using the Photoplethysmography Signal,” Journal of Engineering Research and Sciences, vol. 1, no. 4, pp. 161–174, Apr. 2022, doi: 10.55708/js0104018.
Pressure (BP) waveform is a result of the response of the arteries to the blood ejectionproduced by tant indicator of the state of the cardiovascular system. Currently, its measurement is performed invasively in critically ill patients who need a continuous and real time monitoring of their treatment response, however, it is possible to measure the BP, continuously and non-invasively, in non-critical patients to detect, monitor and control possible hypertensive events. Nevertheless, current non-invasive techniques can cause discomfort in patients and they are not used in critically ill patients. Consequently, non-Invasive and minimally-Intrusive methodologies (nImI) are required to estimate BP and its waveform. In the current study, the performance of machine learning algorithms, specifically the Extreme Learning Machine (ELM) algorithm, is evaluated to estimate both Blood Pressure and its waveform from the Photoplethysmography (PPG) signal and its first derivative’s (VPG) waveforms. A total of 15 healthy volunteers participated in this study. They performed two handgrips, which is isometric maneuver to induce controlled BP rises. The first handgrip is used to train ELM and the second handgrip is used to test the ELM. Our results show that there are high correlation performances (0.98) between the estimated and measured BP waveforms, and a relative error of 3.3 1.4%. An arterial volume-clamp at the middle finger is used as the gold-standard measurement. Meanwhile, BP extreme values estimations, Systolic BP (SBP) and Diastolic BP (DBP), are also performed. ELMs have a performance with an average RMSE of 5.9 ± 2.7 mmHG f and 4.8 ± 2.0 mmHg for DBP and, an average relative error of 5.0 ± 2.7% for SBP and 7.0 ± 4.0% for DBP.
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