Applications of heartbeat complexity analysis to depression and bipolar disorder

Abstract

Nonlinear digital signal processing methods addressing system complexity have provided useful computational tools for helping in the diagnosis and treatment monitoring of a wide range of pathologies. In particular, heartbeat complexity measures have been successful in characterizing patients with mental disorders such as Major Depression and Bipolar Disorder. In this chapter, we describe the use of standard complexity measures such as sample entropy and multiscale entropy, as well as instantaneous measures of entropy to characterize pathological mood states when patients undergo affective elicitation or longterm monitoring. Results demonstrate that complexity measures of cardiovascular dynamics can be promising and viable tools to support clinical decision in mental health, improving on the diagnosis and management of psychiatric disorders.