Advance continuous monitoring of blood pressure and respiration rate using denoising auto encoder and LSTM

Abstract

The importance of monitoring vital signs is increasing with the increase in the number of elderly people and deaths from chronic diseases worldwide. Various studies have been conducted for vital sign monitoring, and it has been confirmed that the transmit time, gradient, and amplitude of pulse signals are highly correlated with blood pressure (BP) and respiration rate (RR). In this study, a single photoplethysmography (PPG) sensor-based wearable device is designed for the continuous monitoring of BP, RR, and heart rate (HR). The device is designed as an earphone type for fixedness and signal stability; it transmits data via an Arduino-based Bluetooth module for wireless use and supplies power via batteries. Because of the similar frequency range between pulse signals and walking signals, denoising is difficult to perform via frequency analysis, where the noise of the PPG signal is isolated via denoising long short-term memory (LSTM) auto encoder. The gradient element, HR, and envelope are extracted as features from the denoised PPG signal, and BP regression models and RR measurement algorithms are designed based on these features. Finally, the reference vital signs and signals measured by the device are compared to verify the accuracy of the device. Results show that the average errors for diastolic blood pressure (DBP), systolic blood pressure (SBP) and RR are 3.93%, 6.38%, and 8.95%, respectively.