Signal Processing and Biomedical Engineering Research
Description
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.
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Persons
Ammar Ahmed, Ph.D., is a radar signal processing engineer at Aptiv, Agoura Hills, CA. He earned his Ph.D. in Electrical Engineering from Temple University under the supervision of Dr. Daniel Zhang, Dr. Dennis Silage, and Dr. Joseph Picone. Dr. Ahmed received a B.Sc. degree in Electrical Engineering from the University of Engineering & Technology, Lahore, Pakistan, in 2009 and an M.S. in Systems Engineering from the Pakistan Institute of Engineering & Applied Sciences, Islamabad, Pakistan, in 2011. He has published over 40 technical papers and holds two patents. His research interests are in signal processing, optimization, and radar systems.
Joseph Picone, Ph.D., is a Professor of Electrical and Computer Engineering at Temple University, where he directs the Institute for Signal and Information Processing and is the Associate Director of the Neural Engineering Data Consortium. Dr. Joseph Picone received his Ph.D. in Electrical Engineering in 1983 from the Illinois Institute of Technology. He has spent significant portions of his career in academia (MS State), research (Texas Instruments, AT&T) and the government (NSA). His primary research interests currently are applications of machine learning in the health sciences. Dr. Picone's research funding sources over the years have included NSF, NIH, DoD, DARPA as well as the private sector. He has also been involved in several startup companies in healthcare. For over 40 years, his research groups have been known for producing many innovative open source materials for education and research, including the first state of the art public domain speech recognition system and the TUH EEG Corpus, which has over 8,000 subscribers (see www.isip.piconepress.com). Dr. Picone is a Senior Member of the IEEE, holds several patents in human language technology.
Content
Introduction.- Signal and image analysis (eeg, ecg, mri).- Machine learning.- Data mining and classification.- Big data.- Index.