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.

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. Covers traditional signal processing topics within biomedicine Promotes collaboration between healthcare practitioners and signal processing researchers Presents tutorials and examples of successful applications.