BSCADND2021: Biomedical signal-based computer aided diagnosis for neurological disorders |
Submission link | https://easychair.org/conferences/?conf=bscadnd2021 |
Abstract registration deadline | July 30, 2021 |
Submission deadline | September 15, 2021 |
Biomedical signals provide unprecedented insight into abnormal or anomalous neurological conditions. The computer-aided diagnosis (CAD) system plays a key role in detecting neurological abnormalities and improving diagnosis and treatment consistency in medicine. This book covers different aspects of biomedical signals-based systems used in the automatic detection/identification of neurological disorders (ND). Several biomedical signals are introduced and analyzed, including Electroencephalogram (EEG), Electrocardiogram (ECG), Heart Rate (HR), Magnetoencephalogram (MEG), and Electromyogram (EMG). It explains the role of the CAD system in processing biomedical signals and the application to neurological disorder diagnosis. The book provides the basics of biomedical signal processing, optimization methods, and machine learning/deep learning techniques used in designing CAD systems for neurological disorders.
Editors
- Dr.M.Murugappn, Kuwait College of Science of Technology (A Private University), Doha, Kuwait.
- Dr. Yuvaraj Rajamanickam, National Institute of Education, Nanyang Technological University, Singapore
Submission Guidelines
- MS Word, single column, Times New Roman, 11 font size, 1.15 spacing.
- References must follow springer style.
- Length of the chapter: 20-25 pages (11550 words (approx.)) including references, tables, figures.
- Similarity index should be less than 10 % (without reference).
- All figures must be supplied as final artwork (e.g., JPEG, PNG, TIFF, etc.) at high-resolution.
Highlights
- No publications fees or article processing fees.
- All manuscripts are accepted based on a double-blind peer review editorial process.
- Indexed by Web of Science, Scopus, Google Scholar.
List of Topics
- Introduction to neurological disorders (ND) and biomedical signal processing.
- Basic principles of CAD in ND diagnosis.
- Characterization of biomedical signals in ND.
- Feature engineering and optimization.
- Machine learning techniques in CAD for ND
- Deep learning algorithms in CAD for ND
- Neural network applications in CAD for ND
- Fuzzy logic and optimization techniques
- Supervised and unsupervised learning
- Case studies related to ND diagnosis.
- Challenges in the CAD for ND
- Advances and trends in CAD for ND diagnosis.
- Open Challenges in CAD in ND diagnosis
All questions about submissions should be emailed to bookchapter.bscad@gmail.com and CC to m.murugappan@kct.edu.kw; yuvaraj.rajamanickam@nie.edu.sg