Machine Learning in Biomedical Signal Processing

Dear Colleagues,

Biomedical signals—electrocardiograms (ECGs), electroencephalograms (EEGs), electromyograms (EMGs), photoplethysmograms (PPGs), and other physiological recordings—are increasingly acquired through diverse sensing technologies, including wearable, implantable, and mobile health devices. These advances enable continuous monitoring and real-time applications but also introduce challenges related to signal quality, calibration, inter-subject variability, high dimensionality, and robustness under real-world conditions.

To address these challenges, researchers are turning to advanced machine learning methods, ranging from classical approaches to modern deep learning architectures and hybrid models. These methods enhance the value of sensor-acquired signals by enabling robust preprocessing, efficient feature extraction, improved classification and prediction, and better generalization across heterogeneous populations and acquisition contexts.

The focus of this Special Issue will be on novel machine learning methodologies and their applications in biomedical signal processing, with a particular emphasis on the interplay between algorithms and sensing technologies. Topics of interest include (but are not limited to) the following:

• The integration of multimodal biomedical signals and sensor data through machine learning models;
• Preprocessing and denoising strategies tailored to machine learning pipelines;
• Feature engineering and representation learning from multimodal biomedical sensor data;
• Deep learning and advanced neural network architectures (CNNs, RNNs, transformers, graph neural networks) for biomedical signal analysis;
• Explainable and interpretable machine learning models for biomedical signal analysis;
• Transfer learning, domain adaptation, and federated learning across heterogeneous sensing environments;
• Machine learning applications for real-time monitoring, diagnosis, prognosis, and decision support.

Application areas may include (but are not limited to): cardiology, neurology, rehabilitation, mental health, telemedicine, ambient assisted living, and human–computer interaction. Contributions that highlight novel datasets, open-source tools, and clinically validated results are particularly encouraged.

Both original research articles and review papers are welcome.

Dr. Alan Jović
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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.