Please use this identifier to cite or link to this item:
Title: Nonlinear digital signal processing in mental health : Characterization of major depression using instantaneous entropy measures of heartbeat dynamics
Authors: Valenza, Gaetano
García, Ronald G.
Citi, Luca
Scilingo, Enzo P.
Tomaz, Carlos A.
Barbieri, Riccardo
Issue Date: 13-Mar-2015
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 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 non linear point-process models might contribute to devising useful computational tools for care in mental health
Description: 8 p.
ISSN: 1664-042X
Appears in Collections:DCAGA. Artículos de Investigación

This item is licensed under a Creative Commons License Creative Commons