Statistical model for describing heart rate variability in normal rhythm and atrial fibrillation

TL;DR

This paper introduces a statistical model that characterizes heart rate variability in both normal and atrial fibrillation rhythms, enhancing understanding and prediction of HRV indices across different cardiac conditions.

Contribution

The study develops a novel multivariate statistical model using Mahalanobis distance and k-nearest neighbors to describe HRV indices in NSR and AF, validated with ECG data.

Findings

Model predicts at least 7 HRV indices with high accuracy

Effective differentiation between NSR and AF based on HRV indices

Validation shows strong agreement with statistical tests

Abstract

Heart rate variability (HRV) indices describe properties of interbeat intervals in electrocardiogram (ECG). Usually HRV is measured exclusively in normal sinus rhythm (NSR) excluding any form of paroxysmal rhythm. Atrial fibrillation (AF) is the most widespread cardiac arrhythmia in human population. Usually such abnormal rhythm is not analyzed and assumed to be chaotic and unpredictable. Nonetheless, ranges of HRV indices differ between patients with AF, yet physiological characteristics which influence them are poorly understood. In this study, we propose a statistical model that describes relationship between HRV indices in NSR and AF. The model is based on Mahalanobis distance, the k-Nearest neighbour approach and multivariate normal distribution framework. Verification of the method was performed using 10 min intervals of NSR and AF that were extracted from long-term Holter ECGs. For validation we used Bhattacharyya distance and Kolmogorov-Smirnov 2-sample test in a k-fold procedure. The model is able to predict at least 7 HRV indices with high precision.