Biomedical signal processing : innovation and applications / Iyad Obeid, Ivan Selesnick, Joseph Picone, editors.
Obeid, Iyad, 1975- editor.; Selesnick, Ivan, editor.; Picone, Joseph, editor.
2021
TK5102.9
Available Online

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Title Biomedical signal processing : innovation and applications / Iyad Obeid, Ivan Selesnick, Joseph Picone, editors.
ISBN 9783030674946 (electronic bk.)
3030674940 (electronic bk.)
9783030674939
3030674932
DOI https://doi.org/10.1007/978-3-030-67494-6
Published Cham : Springer, [2021]
Language English
Description 1 online resource (265 pages)
Item Number 10.1007/978-3-030-67494-6 doi
Call Number TK5102.9
Dewey Decimal Classification 621.382/2
Summary 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. 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.
Note Includes index.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed April 21, 2021).
Added Author Obeid, Iyad, 1975- editor.
Selesnick, Ivan, editor.
Picone, Joseph, editor.
Available in Other Form Biomedical Signal Processing.
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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.

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