Study of continuous blood pressure estimation based on pulse transit time, heart rate and photoplethysmography-derived hemodynamic covariates
- 201 Downloads
It is widely recognized that pulse transit time (PTT) can track blood pressure (BP) over short periods of time, and hemodynamic covariates such as heart rate, stiffness index may also contribute to BP monitoring. In this paper, we derived a proportional relationship between BP and PPT−2 and proposed an improved method adopting hemodynamic covariates in addition to PTT for continuous BP estimation. We divided 28 subjects from the Multi-parameter Intelligent Monitoring for Intensive Care database into two groups (with/without cardiovascular diseases) and utilized a machine learning strategy based on regularized linear regression (RLR) to construct BP models with different covariates for corresponding groups. RLR was performed for individuals as the initial calibration, while recursive least square algorithm was employed for the re-calibration. The results showed that errors of BP estimation by our method stayed within the Association of Advancement of Medical Instrumentation limits (− 0.98 ± 6.00 mmHg @ SBP, 0.02 ± 4.98 mmHg @ DBP) when the calibration interval extended to 1200-beat cardiac cycles. In comparison with other two representative studies, Chen’s method kept accurate (0.32 ± 6.74 mmHg @ SBP, 0.94 ± 5.37 mmHg @ DBP) using a 400-beat calibration interval, while Poon’s failed (− 1.97 ± 10.59 mmHg @ SBP, 0.70 ± 4.10 mmHg @ DBP) when using a 200-beat calibration interval. With additional hemodynamic covariates utilized, our method improved the accuracy of PTT-based BP estimation, decreased the calibration frequency and had the potential for better continuous BP estimation.
KeywordsBlood pressure Pulse transit time Pulse wave velocity Hemodynamic MIMIC
This study was funded by National Science and Technology Major Project of the Ministry of Science and Technology of China (No. 2013ZX03005008), and National Key Research and Development Program of China (No. 2017YFF0210803).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants performed by any of the authors. The datasets analysed during the current study are available in the PhysioNet repository, https://physionet.org/physiobank/database/mimicdb/.
- 1.Mendis S, Puska P, Norrving B (2011) Global atlas on cardiovascular disease prevention and control. World Health Organization, GenevaGoogle Scholar
- 2.Mancia G, Fagard R, Narkiewicz K, Redon J, Zanchetti A, Böhm M et al (2013) 2013 ESH/ESC guidelines for the management of arterial hypertension: the Task Force for the Management of Arterial Hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). Blood Press 22(4):193–278CrossRefGoogle Scholar
- 10.Wesseling K, De Wit B, Van der Hoeven G, Van Goudoever J, Settels J (1995) Physiocal, calibrating finger vascular physiology for Finapres. Homeost Health Dis 36(2–3):67Google Scholar
- 14.Poon CC, Zhang Y-T, Liu Y (2006) Modeling of pulse transit time under the effects of hydrostatic pressure for cuffless blood pressure measurements. In: 3rd IEEE/EMBS international summer school on medical devices and biosensors, pp 65–68Google Scholar
- 17.Escobar B, Torres R (2014) Feasibility of non-invasive blood pressure estimation based on pulse arrival time: a MIMIC database study. In: Computing in cardiology 2014, 7–10 Sept 2014, pp 1113–1116Google Scholar
- 18.Poon CCY, Zhang YT (2005) Cuff-less and noninvasive measurements of arterial blood pressure by pulse transit time. In: 2005 IEEE Engineering in Medicine and Biology 27th annual conference, 17–18 Jan 2006, pp 5877–5880)Google Scholar
- 20.Puke S, Suzuki T, Nakayama K, Tanaka H, Minami S (2013) Blood pressure estimation from pulse wave velocity measured on the chest. In: 2013 35th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 3–7 July 2013, pp 6107–6110Google Scholar
- 25.Muehlsteff J, Aubert X, Schuett M (2006) Cuffless estimation of systolic blood pressure for short effort bicycle tests: the prominent role of the pre-ejection period. In: 28th annual international conference of the IEEE Engineering in Medicine and Biology Society, 2006 (EMBS’06), pp 5088–5092Google Scholar
- 26.Jadooei A, Zaderykhin O, Shulgin VI (2013) Adaptive algorithm for continuous monitoring of blood pressure using a pulse transit time. In: 2013 IEEE XXXIII international scientific conference electronics and nanotechnology (ELNANO), 16–19 April 2013, pp 297–301Google Scholar
- 27.Cattivelli FS, Garudadri H (2009) Noninvasive cuffless estimation of blood pressure from pulse arrival time and heart rate with adaptive calibration. In: 2009 sixth international workshop on wearable and implantable body sensor networks, 3–5 June 2009, pp 114–119Google Scholar
- 28.Ma HT (2014) A blood pressure monitoring method for stroke management. Biomed Res Int 2014:7Google Scholar
- 35.Nichols W, O’Rourke M, Vlachopoulos C (2011) McDonald’s blood flow in arteries: theoretical, experimental and clinical principles. Hodder Arnold, LondonGoogle Scholar
- 46.Moody GB, Mark RG (1996) A database to support development and evaluation of intelligent intensive care monitoring. In: Computers in cardiology 1996, 8–11 Sept 1996, pp 657–660Google Scholar
- 49.Wang R, Jia W, Mao ZH, Sclabassi RJ, Sun M (2014) Cuff-free blood pressure estimation using pulse transit time and heart rate. In 2014 12th international conference on signal processing (ICSP), 19–23 Oct 2014, pp 115–118Google Scholar
- 52.Lopez G, Ushida H, Hidaka K, Shuzo M, Delaunay JJ, Yamada I et al (2009) Continuous blood pressure measurement in daily activities. In: 2009 IEEE sensors, 25–28 Oct 2009, pp 827–831Google Scholar
- 60.Muehlsteff J, Aubert XA, Morren G (2008) Continuous cuff-less blood pressure monitoring based on the pulse arrival time approach: the impact of posture. In: 2008 30th annual international conference of the IEEE Engineering in Medicine and Biology Society, 20–25 Aug 2008, pp 1691–1694Google Scholar