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Title
Signal processing in medicine and biology : emerging trends in research and applications / Iyad Obeid, Ivan Selesnick, Joseph Picone, editors.
ISBN
9783030368449
3030368440
Publication Details
Cham : Springer, ©2020.
Language
English
Description
1 online resource (285 pages)
Call Number
TK5102.9
Dewey Decimal Classification
621.3822
Summary
This book covers emerging trends in signal processing research and biomedical engineering, exploring the ways in which signal processing plays a vital role in applications ranging from medical electronics to data mining of electronic medical records. Topics covered include statistical modeling of electroencephalograph data for predicting or detecting seizure, stroke, or Parkinsons; machine learning methods and their application to biomedical problems, which is often poorly understood, even within the scientific community; signal analysis; medical imaging; and machine learning, data mining, and classification. The book features tutorials and examples of successful applications that will appeal to a wide range of professionals and researchers interested in applications of signal processing, medicine, and biology. Covers traditional signal processing topics within biomedicine Promotes collaboration between healthcare practitioners and signal processing researchers Presents tutorials and examples of successful applications.
Note
"This edited volume consists of the expanded versions of the exceptional papers presented at the 2018 IEEE Signal Processing in Medicine and Biology (IEEE SPMB) Symposium held at Temple University in Philadelphia, Pennsylvania, USA"--Page v.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Description based on print version record.
Chapter 1. An Analysis of Automated Parkinsons Diagnosis Using Voice: Methodology and Future Directions
Chapter 2. Noninvasive Vascular Blood Sound Monitoring Through Flexible Microphone
Chapter 3. The Temple University Hospital Digital Pathology Corpus
Chapter 4. Transient Artifacts Suppression in Time Series via Convex Analysis
Chapter 5. The Hurst Exponent A Novel Approach for Assessing Focus During Trauma Resuscitation
Chapter 6. Gaussian Smoothing Filter For Improved EMG Signal Modeling
Chapter 7. Clustering of SCG Events Using Unsupervised Machine Learning
Chapter 8. Deep Learning Approaches for Automated Seizure Detection from Scalp Electroencephalograms.
