Signal Processing and Machine Learning Approaches for Processing Biomedical Sensor Signals
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".
Deadline for manuscript submissions: 30 June 2025 | Viewed by 83
Special Issue Editors
Interests: biomedical signal denoising; machine learning with applications in biomedical signal classification and regression; nonlinear dynamics with applications in EEG and ECG modeling
Special Issues, Collections and Topics in MDPI journals
Interests: neural networks; medical imaging; BCI applications and non-invasive bioinstrumentation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Biomedical sensor signals such as electrocardiograms, photoplethysmograms and electroencephalograms are always affected by noise, meaning that their signal quality is usually very poor. Existing works have focused on the development of denoising algorithms. At present, some advanced signal processing techniques such as wavelet-based denoising methods, empirical mode decomposition-based denoising methods, variational mode decomposition-based denoising methods and singular spectrum analysis-based denoising methods have been developed. In addition, some machine learning approaches such as deep learning approaches have also recently been developed to enable denoising. These techniques can further improve the signal-to-noise ratio of biomedical sensor signals. However, if the signal-to-noise ratio is too low, then the quality of the denoised signals will still be too low for further processing. Hence, some advanced signal processing and machine learning techniques are being developed for the screening of biomedical sensor signals. This call for papers aims to publish articles on novel signal processing and machine learning techniques used for both the denoising and signal screening of biomedical sensor signals.
Prof. Dr. Wing-Kuen Ling
Dr. Steve Ling
Guest Editors
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Keywords
- denoising
- signal screening
- signal processing
- machine learning
- biomedical sensor signals
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