Cardiac Aging Detection Using Complexity Measures

TL;DR

This study explores non-invasive complexity measures like LZ, ApEn, and ETC to detect cardiac aging from heartbeat data, showing that LZ and ETC can differentiate age groups with minimal data samples.

Contribution

It introduces the use of LZ and ETC complexity measures for cardiac aging detection, demonstrating their effectiveness with fewer data samples compared to traditional methods.

Findings

LZ and ETC differentiate young and old subjects with 10 data samples.

ApEn requires at least 15 data samples for differentiation.

Complexity measures are suitable for nonstationary, nonlinear cardiac data.

Abstract

As we age, our hearts undergo changes which result in reduction in complexity of physiological interactions between different control mechanisms. This results in a potential risk of cardiovascular diseases which are the number one cause of death globally. Since cardiac signals are nonstationary and nonlinear in nature, complexity measures are better suited to handle such data. In this study, non-invasive methods for detection of cardiac aging using complexity measures are explored. Lempel-Ziv (LZ) complexity, Approximate Entropy (ApEn) and Effort-to-Compress (ETC) measures are used to differentiate between healthy young and old subjects using heartbeat interval data. We show that both LZ and ETC complexity measures are able to differentiate between young and old subjects with only 10 data samples while ApEn requires at least 15 data samples.