Advances in Multivariate and Multiscale Physiological Signal Analysis

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 1016

Special Issue Editors


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Guest Editor

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Guest Editor
Bioengineering and Robotics Research Center E Piaggio, Università di Pisa, 56123 Pisa, Italy
Interests: biomedical signal processing; heart rate variability; complex systems; time series analysis; wearable systems
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Special Issue Information

Dear Colleagues,

A physiological system is characterized by complex dynamics and nonlinear behavior as a result of its own structural organization and regulatory mechanisms. Moreover, the optimization of physiological states and functions passes through the continuous dynamic interaction of feedback mechanisms across different spatiotemporal scales.

For this reason, advanced multivariate and multiscale signal analysis techniques could strongly improve the information acquired from physiological systems monitoring as a promising avenue to increase the knowledge on biological regulation in healthy and pathological states. Thanks to the latest advances in technology that have provided miniaturized and high-performance acquisition systems, a synchronized multichannel recording of multiple signals—even in wearable and wireless mode—is currently possible.

This Special Issue on “Advances in Multivariate and Multiscale Physiological Signal Analysis” will, therefore, focus on original research papers and comprehensive reviews dealing with computational methodologies and the processing of multivariate signals to quantify specific physiological states, as well as linear and nonlinear dynamics at different time scales in univariate and multichannel recordings.

In this sense, research studies proposing novel multiscale and multivariate quantifiers, coupling/causality indexes, and the application of pattern recognition algorithms to heterogeneous data are relevant.

Topics of interest for this Special Issue include, but are not limited to, cardiovascular pathology, aging, mental diseases, and affective computing.

Dr. Antonio Lanata
Dr. Mimma Nardelli
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

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Published Papers (1 paper)

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16 pages, 533 KiB  
Article
Zooming into the Complex Dynamics of Electrodermal Activity Recorded during Emotional Stimuli: A Multiscale Approach
by Laura Lavezzo, Andrea Gargano, Enzo Pasquale Scilingo and Mimma Nardelli
Bioengineering 2024, 11(6), 520; https://doi.org/10.3390/bioengineering11060520 - 21 May 2024
Viewed by 530
Abstract
Physiological phenomena exhibit complex behaviours arising at multiple time scales. To investigate them, techniques derived from chaos theory were applied to physiological signals, providing promising results in distinguishing between healthy and pathological states. Fractal-like properties of electrodermal activity (EDA), a well-validated tool for [...] Read more.
Physiological phenomena exhibit complex behaviours arising at multiple time scales. To investigate them, techniques derived from chaos theory were applied to physiological signals, providing promising results in distinguishing between healthy and pathological states. Fractal-like properties of electrodermal activity (EDA), a well-validated tool for monitoring the autonomic nervous system state, have been reported in previous literature. This study proposes the multiscale complexity index of electrodermal activity (MComEDA) to discern different autonomic responses based on EDA signals. This method builds upon our previously proposed algorithm, ComEDA, and it is empowered with a coarse-graining procedure to provide a view at multiple time scales of the EDA response. We tested MComEDA’s performance on the EDA signals of two publicly available datasets, i.e., the Continuously Annotated Signals of Emotion (CASE) dataset and the Affect, Personality and Mood Research on Individuals and Groups (AMIGOS) dataset, both containing physiological data recorded from healthy participants during the view of ultra-short emotional video clips. Our results highlighted that the values of MComEDA were significantly different (p-value < 0.05 after Wilcoxon signed rank test with Bonferroni’s correction) when comparing high- and low-arousal stimuli. Furthermore, MComEDA outperformed the single-scale approach in discriminating among different valence levels of high-arousal stimuli, e.g., showing significantly different values for scary and amusing stimuli (p-value = 0.024). These findings suggest that a multiscale approach to the nonlinear analysis of EDA signals can improve the information gathered on task-specific autonomic response, even when ultra-short time series are considered. Full article
(This article belongs to the Special Issue Advances in Multivariate and Multiscale Physiological Signal Analysis)
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