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Electronics
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Emerging Trends in Biomedical Signal and Image Processing
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Emerging Trends in Biomedical Signal and Image Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 9635

Special Issue Editors

Dr. Ahmed Elnakib

E-Mail Website
Guest Editor
Electronics and Communications Engineering, Mansoura Univeristy, Manosura 35516, Egypt
Interests: medical image analysis; biomedical signal processing; computer-aided diagnosis; deep learning; artificial intelligence
Dr. Fahmi Khalifa

E-Mail Website
Guest Editor
Electrical and Computer Engineering Department, Mitchell School of Engineering, Morgan State University, Baltimore, MD 21251, USA
Interests: machine learning; deep learning; artificial intelligence for medical application; biomedical image analysis; signal/image processing; stochastic processes; pattern recognition; image segmentation; image/shape registration; and multimedia encryption
Special Issues, Collections and Topics in MDPI journals
Dr. Ahmed Soliman

E-Mail Website
Guest Editor
Computer Engineering and Control Systems, Mansoura University, Mansoura 35516, Egypt
Interests: machine learning; deep learning; artificial intelligence in healthcare; biomedical image analysis; image processing

Special Issue Information

Dear Colleagues,

In the last decade, emerging tools and trends have been introduced in the field of biomedical signal processing and medical image analysis. The recent advances in medical imaging and biosignal acquisition techniques, combined with the breakthrough of artificial intelligent (AI), have resulted in a huge amount of research work for different medical applications. This research spans various areas, including biomedical signal analysis, functionality assessments of human organs, automated cancer detection and classification, developing different computer-aided diagnostic systems, and analyses of big biomedical data. More specifically, deep learning (DL), a subset of AI, plays a significant role in improving the performance of the current research works in the field of biomedical signal and image processing. This Special Issue focuses on the recent studies and research works on current developments and uses of biological signal and image processing in different medical applications. In addition, this Special Issue addresses the state-of-the-art DL techniques tailored for biomedical signal and image processing, including, but not limited to, transfer learning, end-to-end systems, transformers, federated learning, and explainable AI. We invite both theoretical and practical publications, as well as excellent reviews and survey studies in the field of medical signal and image processing research. Major interests include the following areas, but are not limited to them:

  • Recent trends in biomedical signal processing;
  • Novel techniques for electrocardiogram (ECE) and electroencephalogram (EEG) analyses;
  • Advanced techniques and trends of medical image analysis;
  • Medical applications of artificial intelligence (AI);
  • State-of-the art deep learning (DL) techniques for biomedical signal and image processing;
  • Analysis of big data for biomedical applications;
  • Development of novel computer-aided diagnostic (CAD) systems;
  • Automated cancer detection, classification, and prediction systems;
  • Development of AI models for treatment assessment;
  • Explainable and interpretable AI.

Dr. Ahmed Elnakib
Dr. Fahmi Khalifa
Dr. Ahmed Soliman
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. Electronics 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 2400 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.

Keywords

  • medical image analysis
  • deep learning (DL)
  • biomedical signal processing
  • ECG analysis
  • EEG analysis
  • computer-aided diagnosis (CAD)
  • artificial intelligence (AI)
  • cancer detection
  • automatic diagnosis
  • big data analysis

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Published Papers (3 papers)

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Research

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19 pages, 492 KiB  
Article
Quantifying Coincidence in Non-Uniform Time Series with Mutual Graph Approximation: Speech and ECG Examples
by Piotr Augustyniak and Grażyna Ślusarczyk
Electronics 2023, 12(20), 4228; https://doi.org/10.3390/electronics12204228 - 12 Oct 2023
Viewed by 1269
Abstract
Compressive sensing and arbitrary sampling are techniques of data volume reduction challenging the Shannon sampling theorem and expected to provide efficient storage while preserving original information. Irregularity of sampling is either a result of intentional optimization of a sampling grid or stems from [...] Read more.
Compressive sensing and arbitrary sampling are techniques of data volume reduction challenging the Shannon sampling theorem and expected to provide efficient storage while preserving original information. Irregularity of sampling is either a result of intentional optimization of a sampling grid or stems from sporadic occurrence or intermittent observability of a phenomenon. Quantitative comparison of irregular patterns similarity is usually preceded by a projection to a regular sampling space. In this paper, we study methods for direct comparison of time series in their original non-uniform grids. We also propose a linear graph to be a representation of the non-uniform signal and apply the Mutual Graph Approximation (MGA) method as a metric to infer the degree of similarity of the considered patterns. The MGA was implemented together with four state-of-the-art methods and tested with example speech signals and electrocardiograms projected to bandwidth-related and random sampling grids. Our results show that the performance of the proposed MGA method is comparable to most accurate (correlation of 0.964 vs. Frechet: 0.962 and Kleinberg: 0.934 for speech signals) and to less computationally expensive state-of-the-art distance metrics (both MGA and Hausdorf: O(L1+L2)). Moreover, direct comparison of non-uniform signals can be equivalent to cross-correlation of resampled signals (correlation of 0.964 vs. resampled: 0.960 for speech signals, and 0.956 vs. 0.966 for electrocardiograms) in applications as signal classification in both accuracy and computational complexity. Finally, the bandwidth-based resampling model plays a substantial role; usage of random grid is the primary cause of inaccuracy (correlation of 0.960 vs. for random sampling grid: 0.900 for speech signals, and 0.966 vs. 0.878, respectively, for electrocardiograms). These figures indicate that the proposed MGA method can be used as a simple yet effective tool for scoring similarity of signals directly in non-uniform sampling grids. Full article
(This article belongs to the Special Issue Emerging Trends in Biomedical Signal and Image Processing)
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13 pages, 11481 KiB  
Article
Exploring the Functional Brain Network of Deception in Source-Level EEG via Partial Mutual Information
by Qianruo Kang, Feng Li and Junfeng Gao
Electronics 2023, 12(7), 1633; https://doi.org/10.3390/electronics12071633 - 30 Mar 2023
Cited by 4 | Viewed by 2878
Abstract
In this study, partial mutual information at the source level was used to construct brain functional networks in order to examine differences in brain functions between lying and honest responses. The study used independent component analysis and clustering methods to computationally generate source [...] Read more.
In this study, partial mutual information at the source level was used to construct brain functional networks in order to examine differences in brain functions between lying and honest responses. The study used independent component analysis and clustering methods to computationally generate source signals from EEG signals recorded from subjects who were lying and those who were being honest. Partial mutual information was calculated between regions of interest (ROIs), and used to construct a functional brain network with ROIs as nodes and partial mutual information values as connections between them. The partial mutual information connections that showed significant differences between the two groups of people were selected as the feature set and classified using a functional connectivity network (FCN) classifier, resulting in an accuracy of 88.5%. Analysis of the brain networks of the lying and honest groups showed that, in the lying state, there was increased informational exchange between the frontal lobe and temporal lobe, and the language motor center of the frontal lobe exchanged more information with other brain regions, suggesting increased working and episodic memory load and the mobilization of more cognitive resources. Full article
(This article belongs to the Special Issue Emerging Trends in Biomedical Signal and Image Processing)
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Review

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15 pages, 691 KiB  
Review
Intelligent Eye-Tracker-Based Methods for Detection of Deception: A Survey
by Weronika Celniak, Dominika Słapczyńska, Anna Pająk, Jaromir Przybyło and Piotr Augustyniak
Electronics 2023, 12(22), 4627; https://doi.org/10.3390/electronics12224627 - 12 Nov 2023
Cited by 3 | Viewed by 4806
Abstract
Over the last few years, a large number of studies have been conducted on the monitoring of human behavior remaining beyond conscious control. One area of application for such monitoring systems is lie detection. The most popular method currently used for this purpose [...] Read more.
Over the last few years, a large number of studies have been conducted on the monitoring of human behavior remaining beyond conscious control. One area of application for such monitoring systems is lie detection. The most popular method currently used for this purpose is polygraph examination, which has proven its usefulness in the field and in laboratories, but it is not without its drawbacks. Technological advances in data acquisition and automated analysis have ensured that contactless tools are in high demand in security fields like airport screening or pre-employment procedures. As a result, there has been a shift in interest away from traditional polygraph examinations toward the analysis of facial expressions, voice, and speech patterns, as well as eye-tracking signals to detect deceptive behavior. In this paper, we focus on the last aspect, offer a comprehensive overview of two distinct lie detection methodologies based on eye tracking, and examine the commonly used oculomotor feature analysis. Furthermore, we explore current research directions and their results within the context of their potential applications in the field of forensics. We also highlight future research prospects, suggesting the utilization of eye tracking and scan path interpretation methodologies as a potential fully functional alternative for the conventional polygraph in the future. These considerations refer to legal and ethical issues related to the use of new technology to detect lies. Full article
(This article belongs to the Special Issue Emerging Trends in Biomedical Signal and Image Processing)
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