Machine Learning in Biomedical Signal Processing
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".
Deadline for manuscript submissions: 1 March 2026 | Viewed by 105
Special Issue Editor
Interests: artificial intelligence in medicine; data mining; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
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
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Keywords
- biomedical signal processing
- biomedical time series analysis
- multimodal biomedical signals and sensor data processing
- machine learning
- deep learning
- feature extraction
- feature selection
- hybrid machine learning architecture
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