Analysis based on the Wavelet & Hilbert transforms applied to the full time series of interbeats, for a triad of failures at the heart

TL;DR

This study applies Wavelet and Hilbert transforms to interbeat time series from Physionet-MIT-BIH to analyze heart failure dynamics across three conditions, revealing universal patterns for two diseases and variability for the third.

Contribution

It introduces a comprehensive wavelet-Hilbert analysis of long-term interbeat data for multiple heart failure conditions, demonstrating the method's effectiveness without data segmentation.

Findings

Universal behavior observed for Obstructive Sleep Apnea and Congestive Heart Failure.

No universal pattern found for Atrial Fibrillation.

Analysis confirms the method's applicability to full-length interbeat series.

Abstract

A tetra of sets which elements are time series of interbeats has been obtained from the databank Physionet-MIT-BIH, corresponding to the following failures at the humans' heart: Obstructive Sleep Apnea, Congestive Heart Failure, and Atrial Fibrillation. Those times series has been analyzed statistically using an already known technique based on the Wavelet and Hilbert Transforms. That technique has been applied to the time series of interbeats for 87 patients, in order to find out the dynamics of the heart. The size of the times series varies around 7 to 24 h. while the kind of wavelet selected for this study has been any one of: Daubechies, Biortoghonal, and Gaussian. The analysis has been done for the complet set of scales ranging from: 1-128 heartbeats. Choosing the Biorthogonal wavelet: bior3.1, it is observed: (a) That the time series hasn't to be cutted in shorter periods, with the purpose to obtain the collapsing of the data, (b) An analytical, universal behavior of the data, for the first and second diseases, but not for the third.