title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° ÑÐ¸ÑÑÐµÐ¼Ñ Ð´Ð»Ñ ÐºÐ¾Ð½ÑÑÐ¾Ð»Ñ ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ñ ÑÐµÑÐ´ÐµÑÐ½Ð¾-ÑÐ¾ÑÑÐ´Ð¸ÑÑÐ¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ñ ÑÐµÐ»Ð¾Ð²ÐµÐºÐ°
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Development of a system for monitoring the human cardiovascular system
Creators: name: ÐÐµÑÐ´Ð¾, ÐÐ¸ÐºÑÐ¾ÑÐ¸Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: Ð¿ÑÐ»ÑÑÐ¾Ð²Ð°Ñ Ð²Ð¾Ð»Ð½Ð°
Ð¿ÑÐ»ÑÑÐ¾ÐºÑÐ¸Ð¼ÐµÑÑ
ÑÐ¿ÐµÐºÑÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð°
Ð³ÑÐ°Ð´Ð¸ÐµÐ½ÑÐ½ÑÐ¹ Ð±ÑÑÑÐ¸Ð½Ð³ Ð½Ð°Ð´ ÑÐµÑÐ°ÑÑÐ¸Ð¼Ð¸ Ð´ÐµÑÐµÐ²ÑÑÐ¼Ð¸
ÑÐ»ÑÑÐ°Ð¹Ð½ÑÐ¹ Ð»ÐµÑ
Ð¼ÐµÑÐ¾Ð´ Ð¾Ð¿Ð¾ÑÐ½ÑÑ Ð²ÐµÐºÑÐ¾ÑÐ¾Ð²
pulse wave
pulse oximeter
spectrogram
gradient boosting over decision trees
random forest
support vector machine

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ñ Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»Ñ ÑÐºÐ¾ÑÐ¾ÑÑÐ¸ Ð¿ÑÐ»ÑÑÐ¾Ð²Ð¾Ð¹ Ð²Ð¾Ð»Ð½Ñ Ð¿Ð¾ Ð·Ð°ÑÐµÐ³Ð¸ÑÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÐ¼ ÑÐ¸Ð³Ð½Ð°Ð»Ð°Ð¼ Ð¾Ñ Ð¿ÑÐ»ÑÑÐ¾ÐºÑÐ¸Ð¼ÐµÑÑÐ°. Ð Ð¿ÑÐ¾ÑÐµÑÑÐµ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¸Ñ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ» Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½ Ð°Ð½Ð°Ð»Ð¸Ð· ÑÐ¿Ð¾ÑÐ¾Ð±Ð¾Ð² Ð²ÑÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð² Ð¸Ð· ÑÐ¾ÑÐ¾Ð¿Ð»ÐµÑÐ¸Ð·Ð¼Ð¾Ð³ÑÐ°Ð¼Ð¼Ñ Ð¿ÑÑÐµÐ¼ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ñ ÑÐ¿ÐµÐºÑÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ñ Ð¸ ÑÐ°ÑÑÐµÑÐ° ÑÑÐ°ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ¸Ñ ÑÐ°ÑÐ°ÐºÑÐµÑÐ¸ÑÑÐ¸Ðº Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½Ð¾Ð³Ð¾ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ. Ð¢Ð°Ðº Ð¶Ðµ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½ Ð°Ð½Ð°Ð»Ð¸Ð· Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð² Ð·Ð°Ð´Ð°ÑÐµ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ ÑÐºÐ¾ÑÐ¾ÑÑÐ¸ Ð¿ÑÐ»ÑÑÐ¾Ð²Ð¾Ð¹ Ð²Ð¾Ð»Ð½Ñ. Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÑÐµÐ´Ñ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ¸ Matlab, ÑÐ·ÑÐºÐ° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Python Ð¸ Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐºÐ¸ sklearn ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð° Ð´Ð»Ñ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ ÑÐºÐ¾ÑÐ¾ÑÑÐ¸ Ð¿ÑÐ»ÑÑÐ¾Ð²Ð¾Ð¹ Ð²Ð¾Ð»Ð½Ñ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ: RF â ÑÐ»ÑÑÐ°Ð¹Ð½ÑÐ¹ Ð»ÐµÑ, SVR â ÑÐµÐ³ÑÐµÑÑÐ¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð¼ Ð¾Ð¿Ð¾ÑÐ½ÑÑ Ð²ÐµÐºÑÐ¾ÑÐ¾Ð² Ð¸ GBR â Ð³ÑÐ°Ð´Ð¸ÐµÐ½ÑÐ½ÑÐ¹ Ð±ÑÑÑÐ¸Ð½Ð³. ÐÐ»Ñ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¸Ñ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ñ ÑÐ³ÐµÐ½ÐµÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÐµ Ð´Ð°Ð½Ð½ÑÐµ Ð³ÐµÐ¼Ð¾Ð´Ð¸Ð½Ð°Ð¼Ð¸ÐºÐ¸ Ð¸Ð· Ð¾ÑÐºÑÑÑÐ¾Ð¹ Ð±Ð°Ð·Ñ Ð´Ð°Ð½Ð½ÑÑ. ÐÑÐ»Ð¾ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ ÐºÐ°ÑÐµÑÑÐ²Ð° Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ð¹ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¸ Ð²ÑÐ±ÑÐ°Ð½Ð° Ð¼Ð¾Ð´ÐµÐ»Ñ Ñ Ð½Ð°Ð¸Ð»ÑÑÑÐ¸Ð¼ ÐºÐ°ÑÐµÑÑÐ²Ð¾Ð¼ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ð¹ - SVR, Ð° ÑÐ°ÐºÐ¶Ðµ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð¾ÑÐ±Ð¾ÑÐ° Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð² Ð¸ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½ Ð°Ð½Ð°Ð»Ð¸Ð· Ð·Ð°Ð²Ð¸ÑÐ¸Ð¼Ð¾ÑÑÐ¸ ÐºÐ°ÑÐµÑÑÐ²Ð° Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¾Ñ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð° Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÐ¼ÑÑ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð².
description: This work is devoted to the study of the application of machine learning methods to solve the problem of calculating the pulse wave velocity index based on regis-tered signals from a pulse oximeter. In the course of the work, an analysis of methods for isolating features from a photoplethysmogram was carried out by calculating the spectrogram and calculating the statistical characteristics of the resulting image. The analysis of machine learning algorithms for use in the task of predicting the pulse wave velocity is also carried out. In the course of work using the Matlab development environment, the Python programming language and the sklearn library, a program was developed to deter-mine the pulse wave velocity using machine learning models: RF â random forest, SVR â regression by the support vector method and GBR â gradient boosting. Generated hemodynamic data from an open database were used to perform the work. The prediction quality of various models was compared and the model with the best prediction quality, SVR, was selected, as well as an algorithm for feature se-lection was implemented and the dependence of model quality on the number of fea-tures used was analyzed.