Signal Processing and Biomedical Engineering Research
Applications of Machine Learning Based on Big Data Principles
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Beschreibung
Signal Processing and Biomedical Engineering Research: Applications of Machine Learning Based on Big Data Principles contains expanded versions of selected contributions from the 2024 IEEE Signal Processing in Medicine and Biology Symposium (IEEE SPMB 2024) held at Temple University. The symposium covers a wide range of topics in the life sciences and promotes machine learning and big data applications in bioengineering. The topics covered include signal and image analysis (e.g., EEG, ECG, MRI), machine learning, data mining, and classification, big data resources and applications, applications of quantum computing, digital pathology, computational biology, and genomics, genetics, and proteomics. The book features detailed review articles, tutorials, and examples of successful applications that will appeal to professionals and researchers in signal processing, medicine, and biology. It also provides an easy-to-understand introduction to various bioengineering topics for students and professionals new to the field, and essential algorithmic details on valuable benchmarks for professionals active in the field.
- Presents an interdisciplinary look at research trends in signal processing and biomedicine;
- Promotes collaboration between practitioners and researchers;
- Includes tutorials and examples of successful applications.
Signal Processing and Biomedical Engineering Research: Applications of Machine Learning Based on Big Data Principles contains expanded versions of selected contributions from the 2024 IEEE Signal Processing in Medicine and Biology Symposium (IEEE SPMB 2024) held at Temple University. The symposium covers a wide range of topics in the life sciences and promotes machine learning and big data applications in bioengineering. The topics covered include signal and image analysis (e.g., EEG, ECG, MRI), machine learning, data mining, and classification, big data resources and applications, applications of quantum computing, digital pathology, computational biology, and genomics, genetics, and proteomics. The book features detailed review articles, tutorials, and examples of successful applications that will appeal to professionals and researchers in signal processing, medicine, and biology. It also provides an easy-to-understand introduction to various bioengineering topics for students and professionals new to the field, and essential algorithmic details on valuable benchmarks for professionals active in the field.
Presents an interdisciplinary look at research trends in signal processing and biomedicine Promotes collaboration between practitioners and researchers Includes tutorials and examples of successful applications
Autor*in
Ammar Ahmed
Themen in »Signal Processing and Biomedical Engineering Research«
Artificial Intelligence (AI) Deep Learning (DL) Digital Electroencephalography (EEG) Digital Pathology Explainable AI (XAI) Functional Near Infrared Spectroscopy (fNIRS) Health Sciences Applications of Machine Learning Machine Learning (ML) Magnetic Resonance Imaging Neurodegenerative Diseases Signal Processing