Main description:

Signal Processing in Medicine and Biology: Innovations in Big Data Processing provides an interdisciplinary look at state-of-the-art innovations in biomedical signal processing, especially as it applies to large data sets and machine learning. Chapters are presented with detailed mathematics and complete implementation specifics so that readers can completely master these techniques. The book 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 at the intersection between healthcare, engineering, and computer science.

Contents:

Low Latency Real-Time Seizure Detection Using Transfer Deep Learning.- A Feature Learning Approach Based on Multimodal Human Body Data for Emotion Recognition.- Wavelet-Based Convolutional Neural Network for Parkinson's Disease Detection in Resting-State Electroencephalography.- Monitoring of Auditory Discrimination Therapy for Tinnitus Treatment Based on Event-Related (De-) Synchronization Maps.- Spatial Distribution of Seismocardiographic Signal Clustering.- Noninvasive Detection of Elevated Intracranial Pressure Using Spectral Analysis of Tympanic Membrane Pulsation Signals.- The Temple University Digital Pathology Corpus: The Breast Tissue Subset.