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AI-Based Biomedical Signal Processing
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AI-Based Biomedical Signal Processing

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Biomedical Information and Health".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 635

Special Issue Editors

Dr. Michel Audette

E-Mail Website
Guest Editor
Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA
Interests: computer-assisted medicine (including medical simulation, robotic surgery, and surgical navigation); human performance potentiation; geriatric fall injury detection
Prof. Dr. Jerzy W. Rozenblit

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ 85721, USA
Interests: design and analysis of complex systems; modeling and computer simulation; computer-aided minimally invasive surgery; applications of computer-based technologies to medicine
Dr. Christopher P. Paolini

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, College of Engineering, San Diego State University, 5500 Campanile Drive, San Diego, CA 92182-1309, USA
Interests: Internet of Things; embedded AI; accelerated computing; HPC; scientific computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue on AI-Based Biomedical Signal Processing is dedicated to showcasing state-of-the-art research in deep neural networks (DNNs) of all kinds, including recurrent long-short term memory (LSTM), graph-based, transformer, autoencoder, convolutional, and generative neural networks, applied to the analysis of biomedical signals of all kinds. These signals can span physiological measurements such as electroencephalography (EEG) and electrocardiography (ECG) through kinematics and dynamics measurements, including accelerometry and vision, image, and speech-based biomedical applications. This Special Issue also endorses novel recruitment of high-throughput hardware platforms such as field-programmable gate arrays (FPGAs) and emerging AI processors, particularly supporting real-time processing. It also encourages submissions on new DNN architectures with limited exposure within the biomedical landscape. Finally, this Special Issue also elicits submissions on simulation-based biomedical sensing and device development.

Dr. Michel Audette
Prof. Dr. Jerzy W. Rozenblit
Dr. Christopher P. Paolini
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. Information 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 1800 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

  • deep neural networks
  • graph neural networks
  • generative neural networks
  • EEG
  • ECG
  • biomedical sensing
  • FPGA
  • AI processors
  • medical simulation

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Published Papers (1 paper)

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Research

16 pages, 2784 KB  
Article
Development of Stacked Neural Networks for Application with OCT Data, to Improve Diabetic Retinal Health Care Management
by Pedro Rebolo, Guilherme Barbosa, Eduardo Carvalho, Bruno Areias, Ana Guerra, Sónia Torres-Costa, Nilza Ramião, Manuel Falcão and Marco Parente
Information 2025, 16(8), 649; https://doi.org/10.3390/info16080649 - 30 Jul 2025
Viewed by 397
Abstract
Background: Retinal diseases are becoming an important public health issue, with early diagnosis and timely intervention playing a key role in preventing vision loss. Optical coherence tomography (OCT) remains the leading non-invasive imaging technique for identifying retinal conditions. However, distinguishing between diabetic macular [...] Read more.
Background: Retinal diseases are becoming an important public health issue, with early diagnosis and timely intervention playing a key role in preventing vision loss. Optical coherence tomography (OCT) remains the leading non-invasive imaging technique for identifying retinal conditions. However, distinguishing between diabetic macular edema (DME) and macular edema resulting from retinal vein occlusion (RVO) can be particularly challenging, especially for clinicians without specialized training in retinal disorders, as both conditions manifest through increased retinal thickness. Due to the limited research exploring the application of deep learning methods, particularly for RVO detection using OCT scans, this study proposes a novel diagnostic approach based on stacked convolutional neural networks. This architecture aims to enhance classification accuracy by integrating multiple neural network layers, enabling more robust feature extraction and improved differentiation between retinal pathologies. Methods: The VGG-16, VGG-19, and ResNet50 models were fine-tuned using the Kermany dataset to classify the OCT images and afterwards were trained using a private OCT dataset. Four stacked models were then developed using these models: a model using the VGG-16 and VGG-19 networks, a model using the VGG-16 and ResNet50 networks, a model using the VGG-19 and ResNet50 models, and finally a model using all three networks. The performance metrics of the model includes accuracy, precision, recall, F2-score, and area under of the receiver operating characteristic curve (AUROC). Results: The stacked neural network using all three models achieved the best results, having an accuracy of 90.7%, precision of 99.2%, a recall of 90.7%, and an F2-score of 92.3%. Conclusions: This study presents a novel method for distinguishing retinal disease by using stacked neural networks. This research aims to provide a reliable tool for ophthalmologists to improve diagnosis accuracy and speed. Full article
(This article belongs to the Special Issue AI-Based Biomedical Signal Processing)
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