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      An iPhone Application for Blood Pressure Monitoring via the Oscillometric Finger Pressing Method

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          Abstract

          We developed an iPhone X application to measure blood pressure (BP) via the “oscillometric finger pressing method”. The user presses her fingertip on both the front camera and screen to increase the external pressure of the underlying artery, while the application measures the resulting variable-amplitude blood volume oscillations via the camera and applied pressure via the strain gauge array under the screen. The application also visually guides the fingertip placement and actuation and then computes BP from the measurements just like many automatic cuff devices. We tested the application, along with a finger cuff device, against a standard cuff device. The application yielded bias and precision errors of −4.0 and 11.4 mmHg for systolic BP and −9.4 and 9.7 mmHg for diastolic BP (n = 18). These errors were near the finger cuff device errors. This proof-of-concept study surprisingly indicates that cuff-less and calibration-free BP monitoring may be feasible with many existing and forthcoming smartphones.

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          Smartphone-based blood pressure monitoring via the oscillometric finger-pressing method

          Ramakrishna Mukkamala, Jin-Oh Hahn, Keerthana Natarajan … (2018)
          High blood pressure (BP) is a major cardiovascular risk factor that is treatable, yet hypertension awareness and control rates are low. Ubiquitous BP monitoring technology could improve hypertension management, but existing devices require an inflatable cuff and are not compatible with such anytime, anywhere measurement of BP. We extended the oscillometric principle, which is used by most automatic cuff devices, to develop a cuff-less BP monitoring device using a smartphone. As the user presses her/his finger against the smartphone, the external pressure of the underlying artery is steadily increased while the phone measures the applied pressure and resulting variable-amplitude blood volume oscillations. A smartphone application provides visual feedback to guide the amount of pressure applied over time via the finger pressing and computes systolic and diastolic BP from the measurements. We prospectively tested the smartphone-based device for real-time BP monitoring in human subjects to evaluate usability (n = 30) and accuracy against a standard automatic cuff-based device (n = 32). We likewise tested a finger cuff device, which uses the volume-clamp method of BP detection. About 90% of the users learned the finger actuation required by the smartphone-based device after one or two practice trials. The device yielded bias and precision errors of 3.3 and 8.8 mmHg for systolic BP and –5.6 and 7.7 mmHg for diastolic BP over a 40 to 50 mmHg range of BP. These errors were comparable to the finger cuff device. Cuff-less and calibration-free monitoring of systolic and diastolic BP may be feasible via a smartphone.
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            Health Outcomes Associated With Antihypertensive Therapies Used as First-Line AgentsA Systematic Review and Meta-analysis

            Bruce M. Psaty (1997)
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              Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Predictions on Maximum Calibration Period and Acceptable Error Limits

              Ramakrishna Mukkamala, Jin-Oh Hahn (2018)
              Objective Pulse transit time (PTT) is being widely pursued for ubiquitous blood pressure (BP) monitoring. PTT-based systems may require periodic cuff calibrations but can still be useful for hypertension screening by affording numerous, out-of-clinic measurements that can be averaged. The objective was to predict the maximum calibration period that would not compromise accuracy and acceptable error limits in light of measurement averaging for PTT-based systems. Methods Well-known mathematical models and vast BP data were leveraged. Models relating PTT, age, and gender to BP were employed to determine the maximum time period for the PTT-BP calibration curve to change by <1 mmHg over physiological BP ranges for each age and gender. A model of within-person BP variability was employed to establish the screening accuracy of the conventional cuff-based approach. These models were integrated to investigate the screening accuracy of the average of numerous measurements of a PTT-based system in relation to the accuracy of its individual measurements. Results The maximum calibration period was about 1 year for a 30 year old and declined linearly to about 6 months for a 70 year old. A PTT-based system with precision error of >12 mmHg for systolic BP could achieve the screening accuracy of the cuff-based approach via measurement averaging. Conclusion This theoretical study indicates that PTT-based BP monitoring is viable even with periodic calibration and seemingly high measurement errors. Significance The predictions may help guide the implementation, evaluation, and application of PTT-based BP monitoring systems in practice.
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                Author and article information

                Contributors
                Ramakrishna Mukkamala:
                ORCID: http://orcid.org/0000-0001-8918-4050
                rama@egr.msu.edu
                Journal
                Journal ID (nlm-ta): Sci Rep
                Journal ID (iso-abbrev): Sci Rep
                Title: Scientific Reports
                Publisher: Nature Publishing Group UK (London )
                ISSN (Electronic): 2045-2322
                Publication date (Electronic): 3 September 2018
                Publication date PMC-release: 3 September 2018
                Publication date Collection: 2018
                Volume: 8
                Electronic Location Identifier: 13136
                Affiliations
                ISNI 0000 0001 2150 1785, GRID grid.17088.36, Department of Electrical and Computer Engineering, , Michigan State University, ; East Lansing, MI 48824 United States
                Author information
                http://orcid.org/0000-0001-9541-8191
                http://orcid.org/0000-0001-8918-4050
                Article
                Publisher ID: 31632
                DOI: 10.1038/s41598-018-31632-x
                PMC ID: 6120863
                PubMed ID: 30177793
                SO-VID: da14065c-d396-4a72-8117-c7f95114748a
                Copyright © © The Author(s) 2018
                License:

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 27 July 2018
                Date accepted : 23 August 2018
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000070, U.S. Department of Health &amp; Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB);
                Award ID: EB-018818
                Award Recipient :
                Categories
                Subject: Article
                Custom metadata
                issue-copyright-statement © The Author(s) 2018

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