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 Parkinson's; 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.

<p>Chapter 1. An Analysis of Automated Parkinson's 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.<br></p>