Diagnostics of the arterial hypertension by means of the discriminant analysis analysis of the heart rate variability signals features combinations

The paper presents investigation of the diagnostic possibilities of the arterial hypertension using linear and quadratic combinations of the heart rate variability signals features. For this study, two groups were considered: healthy volunteers and patients suffering from the arterial hypertension of the II-III degree. For the study, features of statistical, geometric, spectral, nonlinear and multifractal methods were investigated. Results of the analysis have shown that among studied combinations four feature sets (heart rate, features of the VLF frequency band and LF/HF ratio) have the highest classification accuracy – 93%. Copyright © 2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

Towards simplifying assessment of athletes physical fitness: Evaluation of the total physical performance by means of machine learning

The paper describes the methodology for the evaluation of the total physical performance of athletes on the basis of simultaneously recorded signals of stabilography and heart rate variability. An objective assessment of the level of physical performance was carried out using testing on the bicycle ergometer. The use of genetic programming and linear discriminant analysis allowed obtaining the set of diagnostically significant features. The set of diagnostically significant features is able to determine the level of physical fitness using only data from stabilographic studies and heart rate variability with an accuracy of at least 97%. Strength and weaknesses of the proposed approach are discussed. © 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved

Towards a decision support system for disorders of the cardiovascular system diagnosing and evaluation of the treatment efficiency

The study describes a preliminary stage of the decision support system development for cardiovascular system disorders. As the clinical model of the disorders, the arterial hypertension was used. The study consisted of two steps: diagnosing of the arterial hypertension and an evaluation of the treatment efficiency during the neuro-electrostimulation application. For the diagnosing part, a clinical study was conducted involving heart rate variability signals recording while performing tilt-test functional load. Performance of different machine learning techniques and feature selection strategies in task of binary classification (healthy volunteers and patients suffering from arterial hypertension) were compared. The genetic programming feature selection and quadratic discriminant analysis classifier reached the highest classification accuracy. Best feature combinations were used to evaluate a treatment efficiency. The results indicate the potential of the proposed decision support system. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserve