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.

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.