Advances in Non-Invasive Biomedical Signal Sensing and Processing with Machine Learning

Chapter 1 Introduction to Non-Invasive Biomedical Signals for Healthcare

Chapter 2 Signal Acquisition Preprocessing and Feature Extraction Techniques for Biomedical Signals

Chapter 3 The Role of EEG as Neuro-Markers for Patients with Depression: A Systematic Review

Chapter 4 Brain-Computer Interface (BCI) Based on the EEG Signal Decomposition Butterfly Optimization and Machine Learning

Chapter 5 Advances in the Analysis of Electrocardiogram in Context of Mass Screening: Technological Trends and Application of AI Anomaly Detection

Chapter 6 Application of Wavelet Decomposition and Ma-Chine Learning for the sEMG Signal Based Ges-Ture Recognition

Chapter 7 Review of EEG Signals Classification Using Machine Learning and Deep-Learning Techniques

Chapter 8 Biomedical Signal Processing and Artificial Intelligence in EOG Signals

Chapter 9 Peak Spectrogram and Convolutional Neural Network-Based Segmentation and Classification for Phonocardiogram Signals

Chapter 10 Eczema Skin Lesions Segmentation Using Deep Neural Network (U-Net)

Chapter 11 Biomedical Signal Processing for Automated Detection of Sleep Arousals Based on Multi-Physiological Signals with Ensemble Learning Methods

Chapter 12 Deep Learning Assisted Biofeedback

Chapter 13 Estimations of Emotional Synchronization Indices for Brain Regions Using Electroencephalogram Signal Analysis

Chapter 14 Recognition Enhancement of Dementia Patients’ Working Memory Using Entropy-Based Features and Local Tangent Space Alignment Algorithm