Modern technologies for recording human physiological parameters are developing very dynamically. The use of optical methods for measuring human physiological parameters, such as glucose, bilirubin, pulse, and blood saturation, have become key tools in non-invasive diagnostics and monitoring of patients' health. These technologies utilize the properties of light to obtain accurate and fast results, which can improve the efficiency of medical services. The article discusses the basic principles of optical biosensors, their application in measuring these physiological parameters and integration with telemedicine diagnostic systems. The prospects for the introduction of the Internet of Things (IoT) and artificial intelligence (AI) to create telemedicine systems that provide continuous real-time monitoring of patients and a rapid response to changes in their health status are also considered. We propose a system structure that allows remote monitoring of key health indicators of patients, especially infants and children, as well as patients with chronic diseases such as diabetes and cardiovascular diseases. The introduction of such technologies helps to increase the availability of medical services, reduce the number of hospitalizations, and improve the quality of life of patients.
KEYWORDS: Interference (communication), Signal processing, Fractal analysis, Digital signal processing, Statistical analysis, Heart, Data modeling, Analytical research, Telecommunications, Diagnostics
The article explores the possibility of applying modifications to the R/S- analysis method for pulsogram processing under conditions where the noise level is unknown. It is shown that a fast algorithm for calculating the Hirst coefficient can be used to estimate the noise level in a pulse signal. The R/S-analysis method for pulsogram processing has been improved by optimizing the initial conditions, which makes it possible to select the required pulse wave registration interval.
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