In recent times, biomedical data analysis combined with machine learning is an important research topic for the development of automated diagnosis systems. The recent advances in the Internet of medical things (IoMT) also enabled the use of smart and wearable sensors for personalized healthcare. The machine learning techniques play a crucial role in IoMT for the development of smart, affordable, and cost-effective healthcare systems.
This Research Topic will help to demonstrate and provide the applications of various machine learning (deep learning) methods for the processing and classification of different biomedical signals such as electrocardiogram (ECG), electroencephalogram (EEG), electrooculogram(EOG), phonocardiogram (PCG), photoplethysmograph (PPG), respiratory rate (RR), Heart rate (HR), ballistocardiograph (BCG), seismocardiogram (SCG), cough sounds, etc. This article collection welcomes high-quality original research and other types of articles on machine learning and deep learning for biomedical data analysis.
We expect submissions relating, but not limited to, the following themes:
1. Machine learning and deep learning for Cardiovascular Signal Processing.
2. Machine Learning and Deep Learning for Neural Signal Processing.
3. Graph Signal Processing and Machine Learning applications using Physiological Signals.
4. Machine learning and Deep learning for Emotion Recognition using Physiological signals.
5. Machine learning and Deep Learning applications for Electromyogram signal processing.
6. Machine Learning and Deep Learning for Bioradar signal Processing
In recent times, biomedical data analysis combined with machine learning is an important research topic for the development of automated diagnosis systems. The recent advances in the Internet of medical things (IoMT) also enabled the use of smart and wearable sensors for personalized healthcare. The machine learning techniques play a crucial role in IoMT for the development of smart, affordable, and cost-effective healthcare systems.
This Research Topic will help to demonstrate and provide the applications of various machine learning (deep learning) methods for the processing and classification of different biomedical signals such as electrocardiogram (ECG), electroencephalogram (EEG), electrooculogram(EOG), phonocardiogram (PCG), photoplethysmograph (PPG), respiratory rate (RR), Heart rate (HR), ballistocardiograph (BCG), seismocardiogram (SCG), cough sounds, etc. This article collection welcomes high-quality original research and other types of articles on machine learning and deep learning for biomedical data analysis.
We expect submissions relating, but not limited to, the following themes:
1. Machine learning and deep learning for Cardiovascular Signal Processing.
2. Machine Learning and Deep Learning for Neural Signal Processing.
3. Graph Signal Processing and Machine Learning applications using Physiological Signals.
4. Machine learning and Deep learning for Emotion Recognition using Physiological signals.
5. Machine learning and Deep Learning applications for Electromyogram signal processing.
6. Machine Learning and Deep Learning for Bioradar signal Processing
