Multivariate discriminant analysis of the electrocardiogram

Abstract

Objectives. The article presents a study of heart rate variability using multivariate discriminant analysis. Representing an effective statistical method of classification, discriminant analysis can be used to divide objects into groups based on differences in the parameters characterizing these objects. The effectiveness of multivariate discriminant analysis, which is actively used in medicine to diagnose cardiovascular pathologies, is due to the wide range of analyzed parameters: statistical, spectral, and autocorrelation. The aim of the work is to identify the parameters of variational pulsometry, which provide the best distinction between healthy patients and patients with arrhythmia, by means of discriminant analysis.Methods. The durations of cardiac intervals of patients aged 63–72 years, which had been placed in the open database of biomedical signals PhysioNet.org, were used as initial data. When selecting the arguments of the discriminant function, priority was given to parameters that were weakly correlated with each other, had a normal distribution, and differed between healthy and ill patients. The statistical significance of differences between the parameters of the two groups was tested using Student’s t-test and Mann–Whitney U test.Results. Two discriminant functions were obtained: the first depended on three time-domain parameters, while the second included one spectral and one autocorrelation parameter in addition to time-domain parameters. In both cases, the average values of the discriminant function for healthy and sick patients were calculated. The statistical significance of differences in the average values of the discriminant function in the two groups was investigated using Student’s t-test.Conclusions. The values of the first discriminant function are shown to differ insignificantly between healthy and sick patients, while the inclusion of autocorrelation and spectral parameters in the number of arguments of the discriminant function provides pronounced and statistically significant differences between patients of the two groups. Thus, the high significance of spectral and autocorrelation parameters in arrhythmia diagnosis was demonstrated.

Keywords

• heart rate variability
• variational pulsometry
• spectral analysis
• autocorrelation analysis
• rr-intervals
• multivariate discriminant analysis
• discriminant function