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.
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.
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.l¾
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