Atypicality for Heart Rate Variability Using a Pattern-Tree Weighting Method

TL;DR

This paper introduces a novel pattern tree method for analyzing heart rate variability, enabling the detection of arrhythmias and unknown patterns through an atypicality framework, advancing cardiovascular diagnostics.

Contribution

It extends Willem's context tree to real-valued data for HRV analysis, providing a universal source coding approach for discovering atypical patterns in cardiovascular data.

Findings

Detected arrhythmias in HRV Holter Monitoring

Discovered unknown patterns in heart rate data

Demonstrated effectiveness of the pattern tree method

Abstract

Heart rate variability (HRV) is a vital measure of the autonomic nervous system functionality and a key indicator of cardiovascular condition. This paper proposes a novel method, called pattern tree which is an extension of Willem's context tree to real-valued data, to investigate HRV via an atypicality framework. In a previous paper atypicality was developed as method for mining and discovery in "Big Data," which requires a universal approach. Using the proposed pattern tree as a universal source coder in this framework led to discovery of arrhythmias and unknown patterns in HRV Holter Monitoring.