Regression and Neural Network Analysis of the System of Cardiorespiratory Synchronism Parameters to Predict the Minimum Limit of the Synchronization Range

Abstract

In order to improve the existing step-by-step principle of determining the limits of the range of cardiorespiratory synchronization (CRS) and the regulatory and adaptive capabilities of the human body, a regression analysis of measurements of the CRS parameters system was carried out. Statistical prediction of the minimum limit of the synchronization range in the CRS method was performed using linear, nonlinear multiple regression models, as well as neural network regression. The following parameters were used as the basis for predicting the minimum limit of the synchronization range: initial heart rate, initial respiratory rate, age and gender. The variable initial heart rate contributed the most to all regression models. A comparative analysis of the quality indicators of all models showed that the prediction of the minimum limit of the synchronization range can be carried out both by linear multiple regression and using neural network regression implemented by means of a 3-4-1 multilayer perceptrons. However, having already had a NN-regression forecast of maximum limit of the synchronization range, as well as a NN-classification of the level of regulatory-adaptive status, the use of NN-regression will be proposed as the main forecasting method for its implementation in the software of the CRS measurement system.