Biomedical Signal Processing, 1st ed. 2021 Innovation and Applications
Langue : Anglais
Coordonnateurs : Obeid Iyad, Selesnick Ivan, Picone Joseph
This book provides an interdisciplinary look at emerging trends in signal processing and biomedicine found at the intersection of healthcare, engineering, and computer science. It examines the vital role signal processing plays in enabling a new generation of technology based on big data, and looks at applications ranging from medical electronics to data mining of electronic medical records. Topics covered include analysis of medical images, machine learning, biomedical nanosensors, wireless technologies, and instrumentation and electrical stimulation. Biomedical Signal Processing: Innovation and Applications 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.
Chapter 1. Multi-CLASS fNIRS Classification of Motor Execution Tasks with Application to Brain Computer Interfaces.- Chapter 2. A Comparative Study of End-to-End Discriminative Deep Learning Models for Knee Joint Kinematic Time Series Classification.- Chapter 3. Nonlinear Smoothing of Data with Random Gaps and Outliers (DRAGO) Improves Estimation of Circadian Rhythm.- Chapter 4. Wearable Smart Garment Devices for Passive Biomedical Monitoring.- Chapter 5. Spatial Distribution of Seismocardiographic Signals.- Chapter 6. Noninvasive Vascular Blood Sound Monitoring Through Flexible Pvdf Microphone.- Chapter 7. Fast Automatic Artifact Annotator for EEG Signals Using Deep Learning.- Chapter 8. Objective evaluation metrics for automatic classification of EEG events.
Iyad Obeid, PhD, 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. 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.
Ivan Selesnick, PhD, is a professor of Electrical and Computer Engineering at NYU Tandon School of Engineering. He received the BS, MEE, and PhD 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.
Joseph Picone, PhD, 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
Presents an interdisciplinary look at research trends in signal processing and biomedicine
Promotes collaboration between healthcare practitioners and signal processing researchers
Includes tutorials and examples of successful applications
Date de parution : 04-2022
Ouvrage de 261 p.
15.5x23.5 cm
Date de parution : 04-2021
Ouvrage de 261 p.
15.5x23.5 cm
Thèmes de Biomedical Signal Processing :
Mots-clés :
Adaptive Filtering; Big Data; Bioinformatics; Biomedical Engineering; Biomedical Instrumentation; Biomedical Nanosensors; Data Classification; Data Mining; Linear Filtering and Prediction; Machine Learning; Medical Imaging; Nonlinear Filtering and Prediction; Signal Analysis; Signal Processing; Time-Frequency
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