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Advances in Multivariate and Multiscale Physiological Signal Analysis

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

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 3507

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Special Issue Editors

Dr. Antonio Lanata

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Guest Editor

Department of Information Engineering, Università degli Studi di Firenze, Firenze, Italy
Interests: wearable system for non-invasive physiological monitoring; statistical and nonlinear biomedical signal processing; affective computing; mood/mental/neurological disorders; human–animal–robot interaction; autonomic nervous system investigation
Special Issues, Collections and Topics in MDPI journals

Dr. Mimma Nardelli

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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
Special Issues, Collections and Topics in MDPI journals

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Published Papers (2 papers)

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Research

19 pages, 16973 KiB

Open AccessArticle

Improving EEG Forward Modeling Using High-Resolution Five-Layer BEM-FMM Head Models: Effect on Source Reconstruction Accuracy

by Guillermo Nuñez Ponasso, William A. Wartman, Ryan C. McSweeney, Peiyao Lai, Jens Haueisen, Burkhard Maess, Thomas R. Knösche, Konstantin Weise, Gregory M. Noetscher, Tommi Raij and Sergey N. Makaroff

Bioengineering 2024, 11(11), 1071; https://doi.org/10.3390/bioengineering11111071 - 26 Oct 2024

Cited by 1 | Viewed by 908

Abstract

Electroencephalographic (EEG) source localization is a fundamental tool for clinical diagnoses and brain-computer interfaces. We investigate the impact of model complexity on reconstruction accuracy by comparing the widely used three-layer boundary element method (BEM) as an inverse method against a five-layer BEM accelerated by the fast multipole method (BEM-FMM) and coupled with adaptive mesh refinement (AMR) as forward solver. Modern BEM-FMM with AMR can solve high-resolution multi-tissue models efficiently and accurately. We generated noiseless 256-channel EEG data from 15 subjects in the Connectome Young Adult dataset, using four anatomically relevant dipole positions, three conductivity sets, and two head segmentations; we mapped localization errors across the entire grey matter from 4000 dipole positions. The average location error among our four selected dipoles is ∼5

m

m

(±2

m

m

) with an orientation error of ∼

12^{\circ}

(±

7^{\circ}

). The average source localization error across the entire grey matter is ∼9

m

m

(±4

m

m

), with a tendency for smaller errors on the occipital lobe. Our findings indicate that while three-layer models are robust under noiseless conditions, substantial localization errors (10–20

m

m

) are common. Therefore, models of five or more layers may be needed for accurate source reconstruction in critical applications involving noisy EEG data. Full article

(This article belongs to the Special Issue Advances in Multivariate and Multiscale Physiological Signal Analysis)

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16 pages, 533 KiB

Open AccessArticle

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 1499

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.

M C o m E D A

) to discern different autonomic responses based on EDA signals. This method builds upon our previously proposed algorithm,

C o m E D A

, and it is empowered with a coarse-graining procedure to provide a view at multiple time scales of the EDA response. We tested

M C o m E D A

’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

M C o m E D A

were significantly different (p-value < 0.05 after Wilcoxon signed rank test with Bonferroni’s correction) when comparing high- and low-arousal stimuli. Furthermore,

M C o m E D A

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