When heart beats differently in depression: a review of HRV measures

TL;DR

This review highlights how nonlinear heart rate variability measures provide more sensitive detection of autonomic nervous system disturbances in depression, with implications for early cardiovascular risk screening using portable ECG devices.

Contribution

It systematically compares conventional and nonlinear HRV analysis methods in depression, emphasizing the superior sensitivity of nonlinear measures for detecting ANS abnormalities.

Findings

Nonlinear HRV measures are more sensitive than traditional methods.

Aberrant ANS dynamics can be detected via ECG analysis.

Portable ECG and telehealth enable continuous monitoring.

Abstract

Background and Objective: The connection between depression and autonomous nervous system (ANS) is well documented in scientific literature. Heart rate variability (HRV) is a rich source of information for studying the dynamics of this relation. Disturbed heart dynamics in depression seriously increases mortality risk. Technical sciences help improve early detection and monitoring and offer more accurate management of treatment. Based on advances in computational power, information theory, complex systems dynamics, and nonlinear analysis applied to physiological complexity, we can now turn to novel biomarkers extracted from electrophysiological signals. This work is a cross-sectional analysis with methodological commentary of application of nonlinear measures of HRV related to depression. Methods: We systematically searched online for papers exploring the connection of depression and HRV, using both conventional and nonlinear analysis. We scrutinized chosen publications and methodologically analyzed and compared them. Results: Sixty-seven publications on the connection of HRV measures and depression meeting our inclusion criteria are selected. The effectiveness of the applied methods of electrocardiogram (ECG) analysis are compared and discussed in the light of detection and prevention of depression-related cardiac diseases. Conclusions: It is clear that aberrated ANS dynamics can be detected via ECG analysis, where nonlinear measures show to be more sensitive and accurate than standard time and spectral ones. With the portable ECG devices and cloud-based telehealth applications, monitoring of outpatients could be done anytime anywhere. We appeal for early screening for cardiovascular abnormalities in depressive patients to prevent possible deleterious events.