Signal Processing in Medicine and Biology

Innovations in Big Data Processing
Herausgegeben:Obeid, Iyad; Picone, Joseph; Selesnick, Ivan

• Broschiertes Buch

Produktbeschreibung

Signal Processing in Medicine and Biology: Innovations in Big Data Processing provides an interdisciplinary look at state-of-the-art innovations in biomedical signal processing, especially as it applies to large data sets and machine learning. Chapters are presented with detailed mathematics and complete implementation specifics so that readers can completely master these techniques. The book presents tutorials and examples of successful applications and will appeal to a wide range of professionals, researchers, and students interested in applications of signal processing, medicine, and biology at the intersection between healthcare, engineering, and computer science.

Produktdetails

• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-031-21238-3
• 1st ed. 2023
• Seitenzahl: 160
• Erscheinungstermin: 10. Februar 2024
• Englisch
• Abmessung: 235mm x 155mm x 9mm
• Gewicht: 254g
• ISBN-13: 9783031212383
• ISBN-10: 303121238X
• Artikelnr.: 69786608

Autorenporträt

¿Iyad Obeid, Ph.D., is an associate professor of Electrical and Computer Engineering at Temple University with a secondary appointment in the Department of Bioengineering. His research interests include neural signal processing, biomedical signal processing, and medical instrumentation. His research in these fields has been funded by NIH, NSF, DARPA, and the US Army. Together with Dr. Joseph Picone, he is the co-founder of the Neural Engineering Data Consortium, whose goal is to provide large, well-curated neural signal data to the biomedical research community. In addition to earlier work on brain-machine interfaces, Dr. Obeid's current research has expanded to include non-parametric unsupervised machine learning as well as concussion and injury assessment instrumentation built using commercial off-the-shelf sensors. 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. His primary expertise is in statistical modeling with applications in signal processing, specifically acoustic modeling in speech recognition. A common theme throughout his research career has been a focus on fundamentally new statistical modeling paradigms. He has been an active researcher in various aspects of speech processing for over 35 years. He currently collaborates with the Temple School of Medicine and has previously collaborated with many academic institutions (e.g., the Linguistic Data Consortium, Johns Hopkins University), government agencies (e.g., Department of Defense, DARPA), and companies (e.g., MITRE, Texas Instruments). The National Science Foundation, DoD, DARPA, and several commercial interests have funded his research. He has published over 200 technical papers and holds eight patents. Ivan Selesnick, Ph.D., is a professorof Electrical and Computer Engineering at the New York University Tandon School of Engineering. He received the BS, MEE, and Ph.D. degrees in Electrical Engineering from Rice University, and joined Polytechnic University in 1997 (now NYU Tandon School of Engineering). He received an Alexander von Humboldt Fellowship in 1997 and a National Science Foundation Career award in 1999. In 2003, he received the Jacobs Excellence in Education Award from Polytechnic University. Dr. Selesnick's research interests are in signal and image processing, wavelet-based signal processing, sparsity techniques, and biomedical signal processing. He became an IEEE Fellow in 2016 and has been an associate editor for the IEEE Transactions on Image Processing, IEEE Signal Processing Letters, IEEE Transactions on Signal Processing, and IEEE Transactions on Computational Imaging.

Inhaltsangabe

Low Latency Real-Time Seizure Detection Using Transfer Deep Learning.- A Feature Learning Approach Based on Multimodal Human Body Data for Emotion Recognition.- Wavelet-Based Convolutional Neural Network for Parkinson's Disease Detection in Resting-State Electroencephalography.- Monitoring of Auditory Discrimination Therapy for Tinnitus Treatment Based on Event-Related (De-) Synchronization Maps.- Spatial Distribution of Seismocardiographic Signal Clustering.- Noninvasive Detection of Elevated Intracranial Pressure Using Spectral Analysis of Tympanic Membrane Pulsation Signals.- The Temple University Digital Pathology Corpus: The Breast Tissue Subset.