Artificial Intelligence for Biomedical Signal Processing, 2nd Edition
A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".
Deadline for manuscript submissions: 30 September 2025 | Viewed by 1608
Special Issue Editors
Interests: sleep apnea; biomedical engineering; biomedical signal processing; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: biomedical signal processing; sleep apnea; deep learning; explainable artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue is the second edition of “Artificial Intelligence for Biomedical Signal Processing” (https://www.mdpi.com/journal/bioengineering/special_issues/B0U20906Z6).
Biomedical signals and images provide meaningful information about the status and function of a biological system. This fact, along with the rapid progress in the field of artificial intelligence (AI), has increased the development of AI-based applications that use this information to automatically diagnose diseases, create personalized medicine systems, and even remotely monitor patients' healthcare.
Therefore, this Special Issue aims to attract researchers with an interest in applying AI methods to different biomedical signals and images (such as electrocardiograms, electroencephalograms, magnetoencephalograms, electromyograms, galvanic skin responses, pulse oximetry, computed tomography scans, magnetic resonance imaging, etc.) to assist physicians in these tasks. Topics of interest include, but are not limited to, the following:
- Applications of AI in biomedical engineering.
- AI-based systems for automatic prognosis and diagnosis of diseases.
- Biomedical signal and image analysis using machine learning and deep learning algorithms.
- Explainable artificial intelligence in biomedicine.
- Remote healthcare monitoring using AI-based systems.
- Biomarkers extracted from biosignals and bioimages through AI techniques.
- Physiological time series forecasting.
Original papers that describe new research on these subjects are welcomed. Your contributions will enhance the development of new biosignal processing methodologies with key implications for medicine and healthcare. We look forward to your participation in this Special Issue.
Dr. Verónica Barroso-García
Dr. Fernando Vaquerizo-Villar
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. 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 2700 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
- aid diagnosis
- automatic diagnosis
- biomedical signals
- biomedical signal processing
- deep learning
- diseases
- explainable artificial intelligence
- machine learning
- physiological time series
- predictive models
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