PPG System Development for the Organism Physiological Parameters Monitoring with Artificial Intelligence Technologies

Abstract

Monitoring such health parameters as cardiac rate (CR), respiration rate (RR), blood pressure (BP), degree of oxygen in blood (SpO2), body temperature and other requires careful approach to design and development of medical devices. New non-invasive methods introduced in measuring human physiological parameters based on photoplethysmography (PPG) demonstrated their significant potential in monitoring the state of an organism, but their use in wearable devices is largely hampered by exposure to motion artifacts. This article presents a device for photoplethysmographic studies using various adaptive algorithms for processing the registered signals. The work uses artificial intelligence technologies to monitor the heart rate exposed to external mechanical and electrical interference worsening accuracy characteristics of the system. Besides, system architecture was developed, and a device model was manufactured, which made it possible to measure the optimal algorithm for digital signal processing. When using the PPG system, methods of adaptive signal processing based on Wiener filters, filters on the method of least squares (MLS) and Kalman filtering were used. To ensure heart rate monitoring with the given accuracy, studies were performed with participation of volunteers, and analysis was carried out of the results of various signal processing algorithms operation. In the course of experimental studies, a method was proposed to estimate the heart rate calculation accuracy and to analyze the external noise filtering efficiency by adaptive algorithms. PPG designed and developed system made it possible to monitor the heart rate with the given accuracy, control the organism current state and could be used as a means of cardiovascular disease diagnostics.