Nonlinear digital signal processing in mental health: characterization of major depression using instantaneous entropy measures of heartbeat dynamics
Name
Valenza-2015-Nonlinear digital si.pdf
Size
360.72 KB
Format
Adobe PDF
Checksum (MD5)
aa695ca5080ca4bf674bc020b43afa4f
Author(s)
Valenza, Gaetano
Garcia, Ronald G.
Citi, Luca
Scilingo, Enzo P.
Tomaz, Carlos A
Barbieri, Riccardo
Date Issued
March 2015
Journal
Frontiers in Physiology
Publisher
Frontiers Research Foundation
Citation
Valenza, Gaetano, Ronald G. Garcia, Luca Citi, Enzo P. Scilingo, Carlos A Tomaz, and Riccardo Barbieri. “Nonlinear Digital Signal Processing in Mental Health: Characterization of Major Depression Using Instantaneous Entropy Measures of Heartbeat Dynamics.” Front. Physiol. 6 (March 13, 2015).
Version
Final published version
Abstract
Nonlinear digital signal processing methods that address system complexity have provided useful computational tools for helping in the diagnosis and treatment of a wide range of pathologies. More specifically, nonlinear measures have been successful in characterizing patients with mental disorders such as Major Depression (MD). In this study, we propose the use of instantaneous measures of entropy, namely the inhomogeneous point-process approximate entropy (ipApEn) and the inhomogeneous point-process sample entropy (ipSampEn), to describe a novel characterization of MD patients undergoing affective elicitation. Because these measures are built within a nonlinear point-process model, they allow for the assessment of complexity in cardiovascular dynamics at each moment in time. Heartbeat dynamics were characterized from 48 healthy controls and 48 patients with MD while emotionally elicited through either neutral or arousing audiovisual stimuli. Experimental results coming from the arousing tasks show that ipApEn measures are able to instantaneously track heartbeat complexity as well as discern between healthy subjects and MD patients. Conversely, standard heart rate variability (HRV) analysis performed in both time and frequency domains did not show any statistical significance. We conclude that measures of entropy based on nonlinear point-process models might contribute to devising useful computational tools for care in mental health.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3389/fphys.2015.00074
