Signal Processing in Medicine and Biology

Signal Processing in Medicine and Biology: Applications of Deep Learning to the Health Sciences presents expanded versions of selected papers from the 2023 IEEE Signal Processing in Medicine and Biology Symposium (IEEE SPMB) at Temple University. The symposium presents multidisciplinary research across a wide range of topics in the life sciences. The Neural Engineering Data Consortium hosts the symposium to promote machine learning and big data applications in bioengineering.
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;
Genomics, genetics, proteomics.
Applications of particular interest at the 2023 symposium included digital pathology, computational biology, genomics, genetics, and proteomics. The book features tutorials and examples of successful applications that will appeal to many professionals and researchers in signal processing, medicine, and biology. For students and professionals new to the field, the book offers an easy-to-understand introduction to various bioengineering topics. For professionals active in the field, it provides essential algorithmic details on valuable benchmarks for the technology.

Ammar Ahmed, Ph.D., is a Radar Signal Processing Engineer at Aptiv in Agoura Hills, CA, USA, where he contributes to the development of advanced mobility solutions, focusing on innovations in autonomous driving and safety systems. He earned his Ph.D. from Temple University, USA, in 2021. From 2011 to 2016, Dr. Ahmed worked as an electrical engineer for the National Tokamak Fusion Program in Pakistan, where he was responsible for developing embedded system design for spherical tokamaks. His research interests include signal processing, data analysis, 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 (ISIP) and the Neural Engineering Data Consortium (NEDC). His primary expertise is in machine learning for applications in the health sciences. A common theme throughout his research career has been the development of big data resources that enable research on advanced statistical modeling paradigms. The data and resources developed by NEDC are used by over ten thousand researchers worldwide. The ISIP website is one of the oldest websites devoted to the development of open source resources. Dr. Picone has been an active researcher in various aspects of speech processing for over 40 years. His research has been funded by government agencies such as DoD, as well as many industrial partners (Texas Instruments, Natus). He has published over 300 technical papers and holds eight patents.