Blood Pressure Monitoring System Using a Two-Channel Ballistocardiogram and Convolutional Neural Networks

Hypertension is a chronic disease that kills 7.6 million people worldwide annually. A continuous blood pressure monitoring system is required to accurately diagnose hypertension. Here, a chair-shaped ballistocardiogram (BCG)-based blood pressure estimation system was developed with no sensors attached to users. Two experimental sessions were conducted with 30 subjects. In the first session, two-channel BCG and blood pressure data were recorded for each subject. In the second session, the two-channel BCG and blood pressure data were recorded after running on a treadmill and then resting on the newly developed system. The empirical mode decomposition algorithm was used to remove noise in the two-channel BCG, and the instantaneous phase was calculated by applying a Hilbert transform to the first intrinsic mode functions. After training a convolutional neural network regression model that predicts the systolic and diastolic blood pressures (SBP and DBP) from the two-channel BCG phase, the results of the first session (rest) and second session (recovery) were compared. The results confirmed that the proposed model accurately estimates the rapidly rising blood pressure in the recovery state. Results from the rest sessions satisfied the Association for the Advancement of Medical Instrumentation (AAMI) international standards. The standard deviation of the SBP results in the recovery session exceeded 0.7.

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The effect of mibefradil (RO 40-5967) on blood pressure data of hypertensive patients with various degree of renal impairment assessed by ambulatory blood pressure monitoring

American Journal of Hypertension ◽

10.1016/0895-7061(95)97611-t ◽

1995 ◽

Vol 8 (4) ◽

pp. 67A

Author(s):

A TISLER

Keyword(s):

Blood Pressure ◽

Renal Impairment ◽

Ambulatory Blood Pressure Monitoring ◽

Ambulatory Blood Pressure ◽

Blood Pressure Monitoring ◽

Pressure Data ◽

Pressure Monitoring ◽

Hypertensive Patients ◽

Blood Pressure Data

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Wireless Photoplethysmography Sensor for Continuous Blood Pressure Biosignal Shape Acquisition

Journal of Sensors ◽

10.1155/2020/7192015 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

C. Bambang Dwi Kuncoro ◽

Win-Jet Luo ◽

Yean-Der Kuan

Keyword(s):

Blood Pressure ◽

Blood Pressure Monitoring ◽

Vital Role ◽

Data Conversion ◽

Pressure Monitoring ◽

Power Efficient ◽

Shape Acquisition ◽

Blood Pressure Data ◽

Commercial Off The Shelf ◽

Shape Sensing

Blood pressure assessment plays a vital role in day-to-day clinical diagnosis procedures as well as personal monitoring. Thus, blood pressure monitoring devices must afford convenience and be easy to use with no side effects on the user. This paper presents a compact, economical, power-efficient, and convenient wireless plethysmography sensor for real-time blood pressure biosignal monitoring. The proposed sensor facilitates blood pressure signal shape sensing, signal conditioning, and data conversion as well as its wireless transmission to a monitoring terminal. Received data can, subsequently, be compiled and stored on a computer via a Wi-Fi module. During monitoring, users can observe blood pressure signals being processed and displayed on the graphical user interface (GUI)—developed using a virtual instrumentation (VI) application. The proposed device comprises a finger clip optical pulse sensor, analogue signal preprocessing, microcontroller, and Wi-Fi module. It consumes approximately 500 mW power when operating in the active mode and synthesized using commercial off-the-shelf (COTS) components. Experimental results reveal that the proposed device is reliable and facilitates efficient blood pressure monitoring. The proposed wireless photoplethysmographic (PPG) sensor is a preliminary (or first) version of the intended device manifestation. It provides raw blood pressure data for further classification. Additionally, the collected data concerning the blood pressure wave shape can be easily analysed for use in other biosignal observations, interpretations, and investigations. The design approach also allows the device to be built into a wearable system for further research purposes.

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