Advanced Machine Intelligence for Biomedical Signal Processing
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".
Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 15634
Special Issue Editors
Interests: computational intelligence with applications in health informatics; bioinformatics; text mining and image/video understanding
Special Issue Information
Dear Colleagues,
Following the recent advances in novel signal processing techniques and methodologies for diagnoses, therapy, monitoring, and rehabilitation, the current medical environment is undergoing great changes. Remarkable progress has been made toward solving practical problems in many fields, such as medicine, digital health, brain science, human–computer interfaces, robots, and biometrics. At the same time, the extensive application of artificial intelligence, big data, and machine learning in the medical field has attracted increasing attention to the design and development of automatic analysis systems.
Compared to the traditional signal processing techniques, machine intelligence, which includes artificial neural networks, pattern recognition, random forest, support vector machines, deep learning, and so on, has proved its effectiveness in solving difficult and complex problems related to biomedical signal processing, analysis, modeling, and classification. Although the current research in this field has shown promising results, several research issues, such as feature selection, class imbalance, and predictive performance still need to be further explored.
This Special Issue focuses on advanced research regarding the potential applications of advanced machine intelligence in biomedical signal processing. Researchers are invited to present original research and their latest findings related to the current trends and challenges in biomedical signal processing based on the algorithms and techniques of artificial intelligence. Topics of interest include, but are not limited to, the following:
- EEG/EMG/EOG/PPG analysis.
- Biomedical data and signal acquisition.
- Machine intelligence for biomedical data analysis, modeling, and classification.
- Machine intelligence for medical image analysis, modeling, and classification.
- Big data analytics for biomedical applications.
- Biomedical applications in physiology, motion control, human–computer interfaces, etc.
- Machine Intelligence for personalized medicine.
Dr. Hasan Ogul
Dr. Suzan Arslanturk
Guest Editors
Manuscript Submission Information
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