Special Issue "Advances in Multivariate Physiological Signal Analysis"

A special issue of Bioengineering (ISSN 2306-5354).

Deadline for manuscript submissions: closed (30 April 2021).

Special Issue Editors

Prof. Dr. Antonio Lanata
E-Mail Website
Guest Editor
Department of Information Engineering, University of Florence, 50139 Florence, 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
E-Mail Website
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 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 highly 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 Physiological Signal Analysis” will, therefore, focus on original research papers and comprehensive reviews dealing with computational methodologies, processing of multivariate signals to quantify specific physiological states as well as linear and nonlinear interactions. 

In this sense, research studies proposing novel multivariate quantifiers and coupling/causality indexes as well as 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.

Prof. Dr. Antonio Lanatà
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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (6 papers)

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Research

Article
Multiscale Entropy Analysis of Heart Rate Variability in Neonatal Patients with and without Seizures
Bioengineering 2021, 8(9), 122; https://doi.org/10.3390/bioengineering8090122 - 09 Sep 2021
Viewed by 1148
Abstract
The complex physiological dynamics of neonatal seizures make their detection challenging. A timely diagnosis and treatment, especially in intensive care units, are essential for a better prognosis and the mitigation of possible adverse effects on the newborn’s neurodevelopment. In the literature, several electroencephalographic [...] Read more.
The complex physiological dynamics of neonatal seizures make their detection challenging. A timely diagnosis and treatment, especially in intensive care units, are essential for a better prognosis and the mitigation of possible adverse effects on the newborn’s neurodevelopment. In the literature, several electroencephalographic (EEG) studies have been proposed for a parametric characterization of seizures or their detection by artificial intelligence techniques. At the same time, other sources than EEG, such as electrocardiography, have been investigated to evaluate the possible impact of neonatal seizures on the cardio-regulatory system. Heart rate variability (HRV) analysis is attracting great interest as a valuable tool in newborns applications, especially where EEG technologies are not easily available. This study investigated whether multiscale HRV entropy indexes could detect abnormal heart rate dynamics in newborns with seizures, especially during ictal events. Furthermore, entropy measures were analyzed to discriminate between newborns with seizures and seizure-free ones. A cohort of 52 patients (33 with seizures) from the Helsinki University Hospital public dataset has been evaluated. Multiscale sample and fuzzy entropy showed significant differences between the two groups (p-value < 0.05, Bonferroni multiple-comparison post hoc correction). Moreover, interictal activity showed significant differences between seizure and seizure-free patients (Mann-Whitney Test: p-value < 0.05). Therefore, our findings suggest that HRV multiscale entropy analysis could be a valuable pre-screening tool for the timely detection of seizure events in newborns. Full article
(This article belongs to the Special Issue Advances in Multivariate Physiological Signal Analysis)
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Article
Mechanical Ventilator Parameter Estimation for Lung Health through Machine Learning
Bioengineering 2021, 8(5), 60; https://doi.org/10.3390/bioengineering8050060 - 07 May 2021
Viewed by 1449
Abstract
Patients whose lungs are compromised due to various respiratory health concerns require mechanical ventilation for support in breathing. Different mechanical ventilation settings are selected depending on the patient’s lung condition, and the selection of these parameters depends on the observed patient response and [...] Read more.
Patients whose lungs are compromised due to various respiratory health concerns require mechanical ventilation for support in breathing. Different mechanical ventilation settings are selected depending on the patient’s lung condition, and the selection of these parameters depends on the observed patient response and experience of the clinicians involved. To support this decision-making process for clinicians, good prediction models are always beneficial in improving the setting accuracy, reducing treatment error, and quickly weaning patients off the ventilation support. In this study, we developed a machine learning model for estimation of the mechanical ventilation parameters for lung health. The model is based on inverse mapping of artificial neural networks with the Graded Particle Swarm Optimizer. In this new variant, we introduced grouping and hierarchy in the swarm in addition to the general rules of particle swarm optimization to further improve its prediction performance of the mechanical ventilation parameters. The machine learning model was trained and tested using clinical data from canine and feline patients at the University of Georgia College of Veterinary Medicine. Our model successfully generated a range of parameter values for the mechanical ventilation applied on test data, with the average prediction values over multiple trials close to the target values. Overall, the developed machine learning model should be able to predict the mechanical ventilation settings for various respiratory conditions for patient’s survival once the relevant data are available. Full article
(This article belongs to the Special Issue Advances in Multivariate Physiological Signal Analysis)
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Article
Spectral Decomposition of the Flow and Characterization of the Sound Signals through Stenoses with Different Levels of Severity
Bioengineering 2021, 8(3), 41; https://doi.org/10.3390/bioengineering8030041 - 19 Mar 2021
Viewed by 1287
Abstract
Treatments of atherosclerosis depend on the severity of the disease at the diagnosis time. Non-invasive diagnosis techniques, capable of detecting stenosis at early stages, are essential to reduce associated costs and mortality rates. We used computational fluid dynamics and acoustics analysis to extensively [...] Read more.
Treatments of atherosclerosis depend on the severity of the disease at the diagnosis time. Non-invasive diagnosis techniques, capable of detecting stenosis at early stages, are essential to reduce associated costs and mortality rates. We used computational fluid dynamics and acoustics analysis to extensively investigate the sound sources arising from high-turbulent fluctuating flow through stenosis. The frequency spectral analysis and proper orthogonal decomposition unveiled the frequency contents of the fluctuations for different severities and decomposed the flow into several frequency bandwidths. Results showed that high-intensity turbulent pressure fluctuations appeared inside the stenosis for severities above 70%, concentrated at plaque surface, and immediately in the post-stenotic region. Analysis of these fluctuations with the progression of the stenosis indicated that (a) there was a distinct break frequency for each severity level, ranging from 40 to 230 Hz, (b) acoustic spatial-frequency maps demonstrated the variation of the frequency content with respect to the distance from the stenosis, and (c) high-energy, high-frequency fluctuations existed inside the stenosis only for severe cases. This information can be essential for predicting the severity level of progressive stenosis, comprehending the nature of the sound sources, and determining the location of the stenosis with respect to the point of measurements. Full article
(This article belongs to the Special Issue Advances in Multivariate Physiological Signal Analysis)
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Article
Deep Neural Networks and Transfer Learning on a Multivariate Physiological Signal Dataset
Bioengineering 2021, 8(3), 35; https://doi.org/10.3390/bioengineering8030035 - 06 Mar 2021
Cited by 2 | Viewed by 1500
Abstract
While Deep Neural Networks (DNNs) and Transfer Learning (TL) have greatly contributed to several medical and clinical disciplines, the application to multivariate physiological datasets is still limited. Current examples mainly focus on one physiological signal and can only utilise applications that are customised [...] Read more.
While Deep Neural Networks (DNNs) and Transfer Learning (TL) have greatly contributed to several medical and clinical disciplines, the application to multivariate physiological datasets is still limited. Current examples mainly focus on one physiological signal and can only utilise applications that are customised for that specific measure, thus it limits the possibility of transferring the trained DNN to other domains. In this study, we composed a dataset (n=813) of six different types of physiological signals (Electrocardiogram, Electrodermal activity, Electromyogram, Photoplethysmogram, Respiration and Acceleration). Signals were collected from 232 subjects using four different acquisition devices. We used a DNN to classify the type of physiological signal and to demonstrate how the TL approach allows the exploitation of the efficiency of DNNs in other domains. After the DNN was trained to optimally classify the type of signal, the features that were automatically extracted by the DNN were used to classify the type of device used for the acquisition using a Support Vector Machine. The dataset, the code and the trained parameters of the DNN are made publicly available to encourage the adoption of DNN and TL in applications with multivariate physiological signals. Full article
(This article belongs to the Special Issue Advances in Multivariate Physiological Signal Analysis)
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Article
Correlating Grip Force Signals from Multiple Sensors Highlights Prehensile Control Strategies in a Complex Task-User System
Bioengineering 2020, 7(4), 143; https://doi.org/10.3390/bioengineering7040143 - 10 Nov 2020
Cited by 2 | Viewed by 1267
Abstract
Wearable sensor systems with transmitting capabilities are currently employed for the biometric screening of exercise activities and other performance data. Such technology is generally wireless and enables the non-invasive monitoring of signals to track and trace user behaviors in real time. Examples include [...] Read more.
Wearable sensor systems with transmitting capabilities are currently employed for the biometric screening of exercise activities and other performance data. Such technology is generally wireless and enables the non-invasive monitoring of signals to track and trace user behaviors in real time. Examples include signals relative to hand and finger movements or force control reflected by individual grip force data. As will be shown here, these signals directly translate into task, skill, and hand-specific (dominant versus non-dominant hand) grip force profiles for different measurement loci in the fingers and palm of the hand. The present study draws from thousands of such sensor data recorded from multiple spatial locations. The individual grip force profiles of a highly proficient left-hander (expert), a right-handed dominant-hand-trained user, and a right-handed novice performing an image-guided, robot-assisted precision task with the dominant or the non-dominant hand are analyzed. The step-by-step statistical approach follows Tukey’s “detective work” principle, guided by explicit functional assumptions relating to somatosensory receptive field organization in the human brain. Correlation analyses (Person’s product moment) reveal skill-specific differences in co-variation patterns in the individual grip force profiles. These can be functionally mapped to from-global-to-local coding principles in the brain networks that govern grip force control and its optimization with a specific task expertise. Implications for the real-time monitoring of grip forces and performance training in complex task-user systems are brought forward. Full article
(This article belongs to the Special Issue Advances in Multivariate Physiological Signal Analysis)
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Article
An Uncertainty Modeling Framework for Intracardiac Electrogram Analysis
Bioengineering 2020, 7(2), 62; https://doi.org/10.3390/bioengineering7020062 - 26 Jun 2020
Viewed by 2072
Abstract
Intracardiac electrograms (EGMs) are electrical signals measured within the chambers of the heart, which can be used to locate abnormal cardiac tissue and guide catheter ablations to treat cardiac arrhythmias. EGMs may contain large amounts of uncertainty and irregular variations, which pose significant [...] Read more.
Intracardiac electrograms (EGMs) are electrical signals measured within the chambers of the heart, which can be used to locate abnormal cardiac tissue and guide catheter ablations to treat cardiac arrhythmias. EGMs may contain large amounts of uncertainty and irregular variations, which pose significant challenges in data analysis. This study aims to introduce a statistical approach to account for the data uncertainty while analyzing EGMs for abnormal electrical impulse identification. The activation order of catheter sensors was modeled with a multinomial distribution, and maximum likelihood estimations were done to track the electrical wave conduction path in the presence of uncertainty. Robust optimization was performed to locate the electrical impulses based on the local conduction velocity and the geodesic distances between catheter sensors. The proposed algorithm can identify the focal sources when the electrical conduction is initiated by irregular electrical impulses and involves wave collisions, breakups, and spiral waves. The statistical modeling framework can efficiently deal with data uncertainties and provide a reliable estimation of the focal source locations. This shows the great potential of a statistical approach for the quantitative analysis of the stochastic activity of electrical waves in cardiac disorders and suggests future investigations integrating statistical methods with a deterministic geometry-based method to achieve advanced diagnostic performance. Full article
(This article belongs to the Special Issue Advances in Multivariate Physiological Signal Analysis)
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