Advanced Sensors and Machine Learning for Biomedical Signal Processing and Medical Devices
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".
Deadline for manuscript submissions: 10 January 2027 | Viewed by 8
Special Issue Editors
Interests: machine learning in bioinformatics and biomedicine; artificial intelligence in healthcare; anomaly detection techniques and applications; gene expression and cancer classification; radiomics and machine learning in medical imaging
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning in bioinformatics and biomedicine; artificial intelligence in healthcare; anomaly detection techniques and applications; gene expression and cancer classification; radiomics and machine learning in medical imaging
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning in bioinformatics and biomedicine; artificial intelligence in healthcare; advanced biomedical signal processing (EEG, ECG, PCG) based on ML; time series analysis and forecasting; optimization and applied mathematics in engineering
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Addressing the growing prevalence of diseases, an aging population, and the global demand for accessible care requires a paradigm shift in healthcare delivery. Recently, the rapid maturation of advanced sensing technology and machine learning (ML) algorithms has provided unprecedented opportunities to tackle these pressing clinical and societal challenges. This Special Issue aims to explore how the integration of intelligent computational models and innovative hardware can enhance diagnostic accuracy, enable continuous patient monitoring, and improve the clinical efficacy of medical devices.
The widespread adoption of biosensors, wearable technologies, and implantable instruments has generated an immense flow of physiological data. However, the true value of these systems lies in translating complex, real-time signals into actionable clinical insights. In this context, ML—particularly deep learning and edge AI—plays a crucial role by enabling robust, personalized, and energy-efficient data interpretation, thereby turning raw data into meaningful biomarkers and precision treatments.
We welcome diverse contributions from both theoretical and applied perspectives. Topics of interest include, but are not limited to:
- Smart sensors and low-power systems for chronic disease monitoring.
- Advanced biomedical signal processing (EEG, ECG, EMG) based on ML.
- AI algorithms for computer-aided diagnosis and precision medicine.
- Wearable medical devices, rehabilitation robotics, and smart prosthetics.
- Integration of edge computing in clinical environments and telemedicine.
- Ethical challenges, privacy, and cybersecurity in biomedical data management.
Prof. Dr. Ignacio Rojas
Prof. Dr. Francisco Ortuño
Prof. Dr. Olga Valenzuela
Guest Editors
Manuscript Submission Information
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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.
Keywords
- machine learning and deep learning
- wearable medical devices
- smart biosensors
- biomedical signal processing
- computer-aided diagnosis
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