  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.
