Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Recent Advances in the Acquisition and Processing of Biomedical Signals and Images

Special Issue Editors
Special Issue Information
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 9336

Share This Special Issue

Special Issue Editors

Dr. Maria Filomena Santarelli

E-Mail Website
Guest Editor

CNR Institute of Clinical Physiology, 56124 Pisa, Italy
Interests: medical image acquisition and reconstruction
Special Issues, Collections and Topics in MDPI journals

Dr. Vincenzo Positano

E-Mail Website
Guest Editor

Fondazione Toscana “G. Monasterio”, 56124 Pisa, Italy
Interests: medical image processing and analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Nicola Vanello

E-Mail Website
Guest Editor

Dipartimento di Ingegneria dell’Informazione, University of Pisa, 56126 Pisa, Italy
Interests: biomedical signal processing; fMRI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical images and signals have the potential to provide information about the anatomical, structural and functional properties of different organs and tissues, from single cells to the whole body. The development of new technologies in the field of biomedical and signal processing has led to a continuous evolution of both instrumentation and methods of analysis.

Furthermore, the development and application of advanced methods and/or models for the extraction of quantitative indices from images and signals is an ever-evolving research activity.

This Special Issue therefore aims to collect original research and review articles on recent advances, technologies, applications and new challenges in the field of biomedical images and signal processing.

Potential topics include but are not limited to:

Innovative detectors in biomedical image and signal acquisition;
New trends in biomedical imaging methodologies, such as magnetic resonance imaging, nuclear medicine imaging, computed tomography and echography;
Hybrid biomedical imaging technologies;
Advances in one-dimensional and multi-dimensional signal processing techniques;
Machine learning and deep learning for biomedical image and signal processing;
Explainable artificial intelligence for biomedical image and signal processing

Dr. Maria Filomena Santarelli
Dr. Vincenzo Positano
Dr. Nicola Vanello
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.

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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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.

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

15 pages, 2508 KiB

Open AccessArticle

Cross-Task Differences in Frontocentral Cortical Activations for Dynamic Balance in Neurotypical Adults

by Robert D. Magruder, Komal K. Kukkar, Jose L. Contreras-Vidal and Pranav J. Parikh

Sensors 2024, 24(20), 6645; https://doi.org/10.3390/s24206645 - 15 Oct 2024

Viewed by 643

Abstract

Although significant progress has been made in understanding the cortical correlates underlying balance control, these studies focused on a single task, limiting the ability to generalize the findings. Different balance tasks may elicit cortical activations in the same regions but show different levels of activation because of distinct underlying mechanisms. In this study, twenty young, neurotypical adults were instructed to maintain standing balance while the standing support surface was either translated or rotated. The differences in cortical activations in the frontocentral region between these two widely used tasks were examined using electroencephalography (EEG). Additionally, the study investigated whether transcranial magnetic stimulation could modulate these cortical activations during the platform translation task. Higher delta and lower alpha relative power were found over the frontocentral region during the platform translation task when compared to the platform rotation task, suggesting greater engagement of attentional and sensory integration resources for the former. Continuous theta burst stimulation over the supplementary motor area significantly reduced delta activity in the frontocentral region but did not alter alpha activity during the platform translation task. The results provide a direct comparison of neural activations between two commonly used balance tasks and are expected to lay a strong foundation for designing neurointerventions for balance improvements with effects generalizable across multiple balance scenarios. Full article

(This article belongs to the Special Issue Recent Advances in the Acquisition and Processing of Biomedical Signals and Images)

► Show Figures

Figure 1

18 pages, 5278 KiB

Open AccessArticle

A Novel Workflow for Electrophysiology Studies in Patients with Brugada Syndrome

by Valentina Hartwig, Maria Sole Morelli, Nicola Martini, Paolo Seghetti, Davide Tirabasso, Vincenzo Positano, Sara Latrofa, Giacomo Mansi, Andrea Rossi, Alberto Giannoni, Alessandro Tognetti and Nicola Vanello

Sensors 2024, 24(13), 4342; https://doi.org/10.3390/s24134342 - 4 Jul 2024

Viewed by 835

Abstract

Brugada Syndrome (BrS) is a primary electrical epicardial disease characterized by ST-segment elevation followed by a negative T-wave in the right precordial leads on the surface electrocardiogram (ECG), also known as the ‘type 1’ ECG pattern. The risk stratification of asymptomatic individuals with spontaneous type 1 ECG pattern remains challenging. Clinical and electrocardiographic prognostic markers are known. As none of these predictors alone is highly reliable in terms of arrhythmic prognosis, several multi-factor risk scores have been proposed for this purpose. This article presents a new workflow for processing endocardial signals acquired with high-density RV electro-anatomical mapping (HDEAM) from BrS patients. The workflow, which relies solely on Matlab software, calculates various electrical parameters and creates multi-parametric maps of the right ventricle. The workflow, but it has already been employed in several research studies involving patients carried out by our group, showing its potential positive impact in clinical studies. Here, we will provide a technical description of its functionalities, along with the results obtained on a BrS patient who underwent an endocardial HDEAM. Full article

(This article belongs to the Special Issue Recent Advances in the Acquisition and Processing of Biomedical Signals and Images)

► Show Figures

Figure 1

18 pages, 1745 KiB

Open AccessArticle

Classification of Sleep Quality and Aging as a Function of Brain Complexity: A Multiband Non-Linear EEG Analysis

by Lucía Penalba-Sánchez, Gabriel Silva, Mark Crook-Rumsey, Alexander Sumich, Pedro Miguel Rodrigues, Patrícia Oliveira-Silva and Ignacio Cifre

Sensors 2024, 24(9), 2811; https://doi.org/10.3390/s24092811 - 28 Apr 2024

Cited by 1 | Viewed by 1358

Abstract

Understanding and classifying brain states as a function of sleep quality and age has important implications for developing lifestyle-based interventions involving sleep hygiene. Current studies use an algorithm that captures non-linear features of brain complexity to differentiate awake electroencephalography (EEG) states, as a function of age and sleep quality. Fifty-eight participants were assessed using the Pittsburgh Sleep Quality Inventory (PSQI) and awake resting state EEG. Groups were formed based on age and sleep quality (younger adults n = 24, mean age = 24.7 years, SD = 3.43, good sleepers n = 11; older adults n = 34, mean age = 72.87; SD = 4.18, good sleepers n = 9). Ten non-linear features were extracted from multiband EEG analysis to feed several classifiers followed by a leave-one-out cross-validation. Brain state complexity accurately predicted (i) age in good sleepers, with 75% mean accuracy (across all channels) for lower frequencies (alpha, theta, and delta) and 95% accuracy at specific channels (temporal, parietal); and (ii) sleep quality in older groups with moderate accuracy (70 and 72%) across sub-bands with some regions showing greater differences. It also differentiated younger good sleepers from older poor sleepers with 85% mean accuracy across all sub-bands, and 92% at specific channels. Lower accuracy levels (<50%) were achieved in predicting sleep quality in younger adults. The algorithm discriminated older vs. younger groups excellently and could be used to explore intragroup differences in older adults to predict sleep intervention efficiency depending on their brain complexity. Full article

(This article belongs to the Special Issue Recent Advances in the Acquisition and Processing of Biomedical Signals and Images)

► Show Figures

Figure 1

13 pages, 1734 KiB

Open AccessArticle

Hardware and Software Setup for Quantitative ²³Na Magnetic Resonance Imaging at 3T: A Phantom Study

by Giulio Giovannetti, Alessandra Flori, Nicola Martini, Filippo Cademartiri, Giovanni Donato Aquaro, Alessandro Pingitore and Francesca Frijia

Sensors 2024, 24(9), 2716; https://doi.org/10.3390/s24092716 - 24 Apr 2024

Cited by 1 | Viewed by 961

Abstract

Magnetic resonance (MR) with sodium (²³Na) is a noninvasive tool providing quantitative biochemical information regarding physiology, cellular metabolism, and viability, with the potential to extend MR beyond anatomical proton imaging. However, when using clinical scanners, the low detectable ²³Na signal and the low ²³Na gyromagnetic ratio require the design of dedicated radiofrequency (RF) coils tuned to the ²³Na Larmor frequency and sequences, as well as the development of dedicated phantoms for testing the image quality, and an MR scanner with multinuclear spectroscopy (MNS) capabilities. In this work, we propose a hardware and software setup for evaluating the potential of ²³Na magnetic resonance imaging (MRI) with a clinical scanner. In particular, the reliability of the proposed setup and the reproducibility of the measurements were verified by multiple acquisitions from a 3T MR scanner using a homebuilt RF volume coil and a dedicated sequence for the imaging of a phantom specifically designed for evaluating the accuracy of the technique. The final goal of this study is to propose a setup for standardizing clinical and research ²³Na MRI protocols. Full article

(This article belongs to the Special Issue Recent Advances in the Acquisition and Processing of Biomedical Signals and Images)

► Show Figures

Figure 1

24 pages, 9326 KiB

Open AccessArticle

Information-Theoretical Analysis of the Cycle of Creation of Knowledge and Meaning in Brains under Multiple Cognitive Modalities

by Joshua J. J. Davis, Florian Schübeler and Robert Kozma

Sensors 2024, 24(5), 1605; https://doi.org/10.3390/s24051605 - 29 Feb 2024

Viewed by 1607

Abstract

It is of great interest to develop advanced sensory technologies allowing non-invasive monitoring of neural correlates of cognitive processing in people performing everyday tasks. A lot of progress has been reported in recent years in this research area using scalp EEG arrays, but the high level of noise in the electrode signals poses a lot of challenges. This study presents results of detailed statistical analysis of experimental data on the cycle of creation of knowledge and meaning in human brains under multiple cognitive modalities. We measure brain dynamics using a HydroCel Geodesic Sensor Net, 128-electrode dense-array electroencephalography (EEG). We compute a pragmatic information (PI) index derived from analytic amplitude and phase, by Hilbert transforming the EEG signals of 20 participants in six modalities, which combine various audiovisual stimuli, leading to different mental states, including relaxed and cognitively engaged conditions. We derive several relevant measures to classify different brain states based on the PI indices. We demonstrate significant differences between engaged brain states that require sensory information processing to create meaning and knowledge for intentional action, and relaxed-meditative brain states with less demand on psychophysiological resources. We also point out that different kinds of meanings may lead to different brain dynamics and behavioral responses. Full article

(This article belongs to the Special Issue Recent Advances in the Acquisition and Processing of Biomedical Signals and Images)

► Show Figures

Figure 1

20 pages, 14704 KiB

Open AccessArticle

Multi-Feature Automatic Extraction for Detecting Obstructive Sleep Apnea Based on Single-Lead Electrocardiography Signals

by Yu Zhou and Kyungtae Kang

Sensors 2024, 24(4), 1159; https://doi.org/10.3390/s24041159 - 9 Feb 2024

Cited by 2 | Viewed by 1679

Abstract

Obstructive sleep apnea (OSA), a prevalent sleep disorder, is intimately associated with various other diseases, particularly cardiovascular conditions. The conventional diagnostic method, nocturnal polysomnography (PSG), despite its widespread use, faces challenges due to its high cost and prolonged duration. Recent developments in electrocardiogram-based diagnostic techniques have opened new avenues for addressing these challenges, although they often require a deep understanding of feature engineering. In this study, we introduce an innovative method for OSA classification that combines a composite deep convolutional neural network model with a multimodal strategy for automatic feature extraction. This approach involves transforming the original dataset into scalogram images that reflect heart rate variability attributes and Gramian angular field matrix images that reveal temporal characteristics, aiming to enhance the diversity and richness of data features. The model comprises automatic feature extraction and feature enhancement components and has been trained and validated on the PhysioNet Apnea-ECG database. The experimental results demonstrate the model’s exceptional performance in diagnosing OSA, achieving an accuracy of 96.37%, a sensitivity of 94.67%, a specificity of 97.44%, and an AUC of 0.96. These outcomes underscore the potential of our proposed model as an efficient, accurate, and convenient tool for OSA diagnosis. Full article

(This article belongs to the Special Issue Recent Advances in the Acquisition and Processing of Biomedical Signals and Images)

► Show Figures

Figure 1

20 pages, 3700 KiB

Open AccessArticle

Three-Dimensional Multi-Modality Registration for Orthopaedics and Cardiovascular Settings: State-of-the-Art and Clinical Applications

by Simone Garzia, Katia Capellini, Emanuele Gasparotti, Domenico Pizzuto, Giuseppe Spinelli, Sergio Berti, Vincenzo Positano and Simona Celi

Sensors 2024, 24(4), 1072; https://doi.org/10.3390/s24041072 - 7 Feb 2024

Cited by 2 | Viewed by 1360

Abstract

The multimodal and multidomain registration of medical images have gained increasing recognition in clinical practice as a powerful tool for fusing and leveraging useful information from different imaging techniques and in different medical fields such as cardiology and orthopedics. Image registration could be a challenging process, and it strongly depends on the correct tuning of registration parameters. In this paper, the robustness and accuracy of a landmarks-based approach have been presented for five cardiac multimodal image datasets. The study is based on 3D Slicer software and it is focused on the registration of a computed tomography (CT) and 3D ultrasound time-series of post-operative mitral valve repair. The accuracy of the method, as a function of the number of landmarks used, was performed by analysing root mean square error (RMSE) and fiducial registration error (FRE) metrics. The validation of the number of landmarks resulted in an optimal number of 10 landmarks. The mean RMSE and FRE values were 5.26 ± 3.17 and 2.98 ± 1.68 mm, respectively, showing comparable performances with respect to the literature. The developed registration process was also tested on a CT orthopaedic dataset to assess the possibility of reconstructing the damaged jaw portion for a pre-operative planning setting. Overall, the proposed work shows how 3D Slicer and registration by landmarks can provide a useful environment for multimodal/unimodal registration. Full article

(This article belongs to the Special Issue Recent Advances in the Acquisition and Processing of Biomedical Signals and Images)

► Show Figures

Journal Menu

Journal Browser

Recent Advances in the Acquisition and Processing of Biomedical Signals and Images

Share This Special Issue

Special Issue Editors

Special Issue Information

Benefits of Publishing in a Special Issue

Published Papers (7 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI