Machine Learning and Pattern Recognition for Biomedical Signals
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".
Deadline for manuscript submissions: 30 August 2025 | Viewed by 55
Special Issue Editors
Interests: human motion analysis; telemedicine; computer vision for digital health; neurosciences; markerless body tracking; human–machine interfaces; exergames for rehabilitation; biosignals for stress and engagement assessments; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: human motion analysis; signal processing; motor control; neurorehabilitation; wearable sensors; artificial intelligence; neuroengineering
Special Issue Information
Dear Colleagues,
Machine learning (ML) models and pattern recognition (PR) techniques are playing (and will continue to play) a key role in transforming and improving the analysis of biomedical signals to promote more innovative paradigms and create powerful models for medical applications and digital healthcare.
Biomedical signals (such as EMG, ECG, EEG, EDA, PPG), like medical images and motion signals, contain a wealth of information that can provide valuable insights into an individual's health status. Machine learning approaches, including deep learning algorithms and advanced statistical models, can help identify complex patterns within complex data that may be missed by traditional analysis. This helps to improve our knowledge and insight into a particular health condition or its deteriorating state. Similarly, pattern recognition techniques can support the automatic interpretation of complex multidimensional signals through feature engineering and clustering methods, increasing the explanatory power of model results and their acceptance by clinicians.
This Special Issue aims to collect recent research on promising and innovative technological and methodological applications of ML and PR to biomedical data, covering a wide range of subtopics. These include applications in a variety of health and pathological conditions, ranging from the early detection and prediction of diseases, more accurate diagnoses, continuous and real-time monitoring, and personalized treatments to prevention and decision support systems. Another field of interest is the integration of ML and PR approaches into wearable sensors, video analysis, and innovative technologies finalized to telemedicine solutions. Other key related challenges include data quality, the availability of large annotated datasets, and supporting model interpretability through explainable AI (XAI) techniques that could improve clinical acceptance and trust in predictive models applied to biomedical signals.
Dr. Claudia Ferraris
Dr. Marco Ghislieri
Guest Editors
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Keywords
- machine learning, pattern recognition, deep learning, and explainable AI for digital healthcare
- biosignal analysis, medical imaging, and human motion analysis
- biosensors, wearable devices, and multimodal data (images, videos) for healthcare applications
- early diagnosis, personalized medicine, disease prevention and progression, and decision support systems
- telemedicine, remote patient monitoring, and digital health solutions
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