Biomedical Signal Processing: A Modern Approach

ISBN-13: 9781032061917 / Angielski

Ganesh Naik; Wellington Pinheiro Dos Santos

Opis

This book presents the theoretical basis and applications of biomedical signal analysis and processing. Initially, the nature of the most common biomedical signals, such as electroencephalography, electromyography, electrocardiography and others, is described. The theoretical basis of linear signal processing is summarized, with continuous and discrete representation, linear filters and convolutions, Fourier and Wavelets transforms. Machine learning concepts are also presented, from classic methods to deep neural networks. Finally, several applications in neuroscience are presented and discussed, involving diagnosis and therapy, in addition to other applications.

Features:

• Explains signal processing of neuroscience applications using modern data science techniques.
• Provides comprehensible review on biomedical signals nature and acquisition aspects.
• Focusses on selected applications of neurosciences, cardiovascular, muscle related biomedical areas.
• Includes computational intelligence, machine learning and biomedical signal processing and analysis.
• Reviews theoretical basis of deep learning and state-of-the-art biomedical signal processing and analysis.

This book is aimed at researchers, graduate students in biomedical signal processing, signal processing, electrical engineering, neuroscience, and computer science.

Spis treści

1. Signal Processing for Understanding Physiological Mechanisms for Information Decoding 2. Automated Recognition of Alzheimer’s Dementia: A Review of Recent Developments in the Context of Interspeech ADReSS Challenges 3. Electrogastrogram Signal Processing: Techniques and Challenges with Application for Simulator Sickness Assessment 4. Impact of Cognitive Demand on the Voice Responses of Parkinson’s Disease and Healthy Cohorts 5. Electroencephalography and Epileptic Discharge Identification 6. A Novel End-to-End Secure System for Automatic Classification of Cardiac Arrhythmia 7. Machine Learning for Detection and Classification of Motor Imagery in Electroencephalographic Signals 8. Emotion Recognition from Electroencephalographic and Peripheral Physiological Signals Using Artificial Intelligence with Explicit Features 9. Identification of Emotion Parameters in Music to Modulate Human Affective States: Towards Emotional Biofeedback as a Therapy Support 10. Updated ICA Weight Matrix for Lower Limb Myoelectric Classification 11. Cortical Correlates of Unilateral Transfemoral Amputees during a Balance Control Task with Vibrotactile Feedback 12. Assessing the Impact of Body Mass Index on Gait Symmetry of Able-Bodied Adults Using Pressure-Sensitive Insole 13. Analysis of Lower Limb Muscle Activities during Walking and Jogging at Different Speeds 14. Biosensors in Optical Devices for Sensing and Signal Processing Applications