Why the study?
Disturbed heart dynamics in depression increase mortality risk, and advances in nonlinear analysis allow exploration of novel HRV biomarkers extracted from electrophysiological signals.
Are nonlinear measures of HRV more sensitive and accurate than standard measures for detecting autonomic nervous system abnormalities in patients with depression?
Are nonlinear measures of HRV more sensitive and accurate than standard measures for detecting autonomic nervous system abnormalities in patients with depression?
Nonlinear HRV measures offer a more sensitive and accurate method than standard measures for detecting autonomic nervous system abnormalities in patients with depression, highlighting the potential for early cardiovascular screening in this population.
HRV metrics may aid early cardiac risk detection in depression; leaves open whether novel computational approaches improve monitoring or outcomes.
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.
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Čukić et al. (2021) studied this question.
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