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http://dx.doi.org/10.6109/jkiice.2022.26.10.1469

Design and Implementation of Mobile Continuous Blood Pressure Measurement System Based on 1-D Convolutional Neural Networks  

Kim, Seong-Woo (Department of Computer Software Engineering, Dong-Eui University)
Shin, Seung-Cheol (Solmitech Co.Ltd.)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.10, 2022 , pp. 1469-1476 More about this Journal
Abstract
Recently, many researches have been conducted to estimate blood pressure using ECG(Electrocardiogram) and PPG(Photoplentysmography) signals. In this paper, we designed and implemented a mobile system to monitor blood pressure in real time by using 1-D convolutional neural networks. The proposed model consists of deep 11 layers which can learn to extract various features of ECG and PPG signals. The simulation results show that the more the number of convolutional kernels the learned neural network has, the more detailed characteristics of ECG and PPG signals resulted in better performance with reduced mean square error compared to linear regression model. With receiving measurement signals from wearable ECG and PPG sensor devices attached to the body, the developed system receives measurement data transmitted through Bluetooth communication from the devices, estimates systolic and diastolic blood pressure values using a learned model and displays its graph in real time.
Keywords
ECG and PPG Signal; Pulse Transit Time; Blood Pressure Estimation; 1-D Convolutional Neural Network;
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Times Cited By KSCI : 1  (Citation Analysis)
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