Literature DB >> 32976107

Wearable Wireless-Enabled Oscillometric Sphygmomanometer: A Flexible Ambulatory Tool for Blood Pressure Estimation.

Nicolas Juteau, Benoit Gosselin.   

Abstract

This article presents the design of an unobtrusive and wireless-enabled blood pressure (BP) monitoring system that is suitable for ambulatory use. By adopting low-profile electromechanical actuators and a compact printed circuit board design, this lightweight device can be worn directly on the occlusive cuff, therefore eliminating the need of a long and obtrusive tubing interconnect between the device and the cuff, as seen in traditional ambulatory BP monitors (ABPM). Instead of executing the BP estimation algorithm directly on the device, the proposed design rather sends the raw oscillometric signal through a Bluetooth Low Energy link, thus granting any Bluetooth-enabled device to gather and process the signal using a dedicated application. This in turn allows to assess several BP estimation algorithms found in the literature without being limited by the device resources. Three of them were tested with the designed prototype and validated with a reference equipment on 11 subjects. Overall, two of the algorithms revealed a mean absolute difference with the reference equipment of less than 5 mmHg and almost zero bias along with a standard deviation of less than 6 mmHg. Reproducibility results shown a mean difference between successive measurements of less than 3.1 mmHg and a standard deviation of less than 2.4 mmHg. The assembled prototype dimensions are 63.8 × 134.8 × 24.8 mm and features an autonomy of 63.1 hours. Comparison with commercial ABPM devices shown that the proposed design is 18% to 33% smaller volume-wise, 5% to 27% weight-wise and height is reduced by 17% to 25%.

Year:  2020        PMID: 32976107     DOI: 10.1109/TBCAS.2020.3026992

Source DB:  PubMed          Journal:  IEEE Trans Biomed Circuits Syst        ISSN: 1932-4545            Impact factor:   3.833


  1 in total

Review 1.  Recent Advances in Non-Invasive Blood Pressure Monitoring and Prediction Using a Machine Learning Approach.

Authors:  Siti Nor Ashikin Ismail; Nazrul Anuar Nayan; Rosmina Jaafar; Zazilah May
Journal:  Sensors (Basel)       Date:  2022-08-18       Impact factor: 3.847

  1 in total

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