Deep Networks for Biosignals
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".
Deadline for manuscript submissions: closed (20 November 2024) | Viewed by 22371
Special Issue Editors
Interests: biosignal processing; biometrics; pattern recognition; wearable embedded system
Special Issues, Collections and Topics in MDPI journals
Interests: pattern recognition; machine learning; biometrics; signal processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Biosignals are electrical, mechanical, thermal, or acoustic signals measured over time from the human body. Various biosignals were discovered that could be derived from the skin or from inside the human body. Initially, analysis of biosignals was performed manually; later, statistical classifiers were applied to support making decisions. However, existing approaches are ineffective for continuously collecting complex, multi-dimensional, real-world data from various sensors. Artificial intelligence helps in the automated and effective analysis of biosignals. Deep networks are one of the well-known techniques used to develop high-level systems for the clustering, detection, and recognition of changes in the human body. Biosignals are generally non-linear, non-stationary, dynamic, and complex; thus, linear and non-linear methods have failed to analyze their characteristics robustly. Furthermore, handcrafted or manually selected features are time-consuming, not optimal, and domain specific. However, deep networks, fed with raw data but not with handcrafted features, perform feature extraction and selection within the network. Deep networks attempt to automatically detect the unobservable patterns needed for analyzing raw data. Multiple layers in the network are interconnected to transform the raw data into a higher level of abstract data representation. Deep networks have gained remarkable performance in various fields, such as computer vision, image understanding, natural language processing, bioinformatics, and physiological analysis for clinical treatments. Thus, deep network models may provide tools and interfaces to complex biosignals for better understanding.
This Special Issue aims to collect advanced research achievements in biosignal processing and analysis using deep networks to address a wide range of application scenarios, which include, but are not limited to, the following:
- Human identification
- Analysis of human movements (gait, hand gesture, etc.)
- Emotion detection and classification
- Human–computer interaction
- Healthcare monitoring (computer-aided diagnostics, remote health system, etc.)
- Various techniques for biosignal processing (ECG, EMG, EEG, PCG, PPG, BCG, etc.)
- Multimodal feature extraction and integration.
- New deep network architectures targeting biosignals
Prof. Dr. Sung Bum Pan
Dr. EunSang Bak
Guest Editors
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Keywords
- deep networks
- human identification
- emotion classification
- movement analysis
- human–computer interaction
- biosignal processing
- multimodal feature integration
- healthcare system
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