Biomedical Imaging, Sensing and Signal Processing—2nd Edition

Dear Colleagues,

Following the success of our previous Special Issue “Biomedical Imaging, Sensing and Signal Processing”, we would like to invite our colleagues once again to contribute their expertise, insights and findings in the form of original research articles and reviews for the current Special Issue.

Biomedical imaging is a dynamic field that has led to significant improvements in our understanding of body and brain functions, developing medicine, developing rehabilitation methods and many other areas relevant to health and wellbeing. Biomedical imaging includes a diverse array of modalities such as X-ray, magnetic resonance imaging, computed tomography, ultrasound, electroencephalogram, functional Near-Infrared Spectroscopy and positron emission tomography. These imaging techniques provide detailed visualizations of internal anatomical structures, facilitating non-invasive diagnosis and precise medical interventions.

Sensing technologies enable us to capture real-time data related to physiological states, biomarkers and environmental factors. This involves the usage of wearable devices, biosensors and other cutting-edge technologies to monitor and analyze vital signs, allowing for personalized and proactive healthcare.

It is undeniable that signal processing and machine learning methods play a pivotal role in removing noise and extracting meaningful information from biomedical data. Techniques such as signal filtering, feature extraction and pattern recognition enhance the accuracy of diagnostics and enable the development of smart healthcare systems.

The objective of this Special Issue is to attract the most recent research and findings in the design, development and experimentation of healthcare-related technologies and computational methodologies. This Special Issue welcomes contributions that engage but are not limited to any of the following topics:

• Biomedical signal and image processing
• Smart monitoring and assisted living systems
• Deep learning for healthcare data
• Sensor fusion of biomedical data
• Brain Computer Interface
• Mental health
• Computational neuroscience
• Electroencephalogram
• Magnetic resonance spectroscopy
• Functional magnetic resonance spectroscopy
• Functional near-infrared spectroscopy
• Magnetoencephalogram
• Electromyography
• Health signal processing/monitoring
• Machine learning and artificial intelligence applications in health and wellbeing
• Physiological signal processing

Dr. Saideh Ferdowsi
Guest Editor

Manuscript Submission Information

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