Application of the Discriminant Analysis for Diagnostics of the Arterial Hypertension - Analysis of Short-Term Heart Rate Variability Signals

Kublanov, Vladimir S.; Dolganov, Anton Yu.; Borisov, Vasilii I.

doi:10.5220/0006044000450052

Cited by 3 publications

(1 citation statement)

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“…In one of the previous works, the investigation of the linear and quadratic discriminant analysis was carried out, implying the study of arterial hypertension diagnostic using single features of short-term HRV signals. In that work, the evaluation of the features and the evaluation of the classifier efficacy were carried out by means of an in-house software produced in MATLAB [ 10 ]. In the present paper, the machine learning methods were implemented in the python.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics

Kublanov

Dolganov

Belo

et al. 2017

Applied Bionics and Biomechanics

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The paper presents results of machine learning approach accuracy applied analysis of cardiac activity. The study evaluates the diagnostics possibilities of the arterial hypertension by means of the short-term heart rate variability signals. Two groups were studied: 30 relatively healthy volunteers and 40 patients suffering from the arterial hypertension of II-III degree. The following machine learning approaches were studied: linear and quadratic discriminant analysis, k-nearest neighbors, support vector machine with radial basis, decision trees, and naive Bayes classifier. Moreover, in the study, different methods of feature extraction are analyzed: statistical, spectral, wavelet, and multifractal. All in all, 53 features were investigated. Investigation results show that discriminant analysis achieves the highest classification accuracy. The suggested approach of noncorrelated feature set search achieved higher results than data set based on the principal components.

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Section: Introductionmentioning

confidence: 99%