Valenza, G and Garcia, RG and Citi, L and Scilingo, EP and Tomaz, CA and Barbieri, R (2015) Nonlinear digital signal processing in mental health: characterization of major depression using instantaneous entropy measures of heartbeat dynamics. Frontiers in Physiology, 6 (74). 74-. DOI https://doi.org/10.3389/fphys.2015.00074
Valenza, G and Garcia, RG and Citi, L and Scilingo, EP and Tomaz, CA and Barbieri, R (2015) Nonlinear digital signal processing in mental health: characterization of major depression using instantaneous entropy measures of heartbeat dynamics. Frontiers in Physiology, 6 (74). 74-. DOI https://doi.org/10.3389/fphys.2015.00074
Valenza, G and Garcia, RG and Citi, L and Scilingo, EP and Tomaz, CA and Barbieri, R (2015) Nonlinear digital signal processing in mental health: characterization of major depression using instantaneous entropy measures of heartbeat dynamics. Frontiers in Physiology, 6 (74). 74-. DOI https://doi.org/10.3389/fphys.2015.00074
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.
Item Type: | Article |
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Uncontrolled Keywords: | nonlinear; heart rate variability; instantaneous entropy; point process; Wiener-Volterra series; Laguerre expansion |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > R Medicine (General) |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 18 May 2015 11:06 |
Last Modified: | 23 Oct 2024 05:13 |
URI: | http://repository.essex.ac.uk/id/eprint/13750 |
Available files
Filename: fphys-06-00074.pdf
Licence: Creative Commons: Attribution 3.0