Photoplethysmography-Based Continuous Systolic Blood Pressure Estimation Method for Low Processing Power Wearable Devices

Author:

Gircys Rolandas,Liutkevicius AgniusORCID,Kazanavicius Egidijus,Lesauskaite Vita,Damuleviciene GyteORCID,Janaviciute AudroneORCID

Abstract

Regardless of age, it is always important to detect deviations in long-term blood pressure from normal levels. Continuous monitoring of blood pressure throughout the day is even more important for elderly people with cardiovascular diseases or a high risk of stroke. The traditional cuff-based method for blood pressure measurements is not suitable for continuous real-time applications and is very uncomfortable. To address this problem, continuous blood pressure measurement methods based on photoplethysmogram (PPG) have been developed. However, these methods use specialized high-performance hardware and sensors, which are not available for common users. This paper proposes the continuous systolic blood pressure (SBP) estimation method based on PPG pulse wave steepness for low processing power wearable devices and evaluates its suitability using the commercially available CMS50FW Pulse Oximeter. The SBP estimation is done based on the PPG pulse wave steepness (rising edge angle) because it is highly correlated with systolic blood pressure. The SBP estimation based on this single feature allows us to significantly reduce the amount of data processed and avoid errors, due to PPG pulse wave amplitude changes resulting from physiological or external factors. The experimental evaluation shows that the proposed SBP estimation method allows the use of off-the-shelf wearable PPG measurement devices with a low sampling rate (up to 60 Hz) and low resolution (up to 8-bit) for precise SBP measurements (mean difference MD = −0.043 and standard deviation SD = 6.79). In contrast, the known methods for continuous SBP estimation are based on equipment with a much higher sampling rate and better resolution characteristics.

Funder

Lietuvos Mokslo Taryba

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

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