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

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

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to Non-Invasive Biomedical Signals for Healthcare
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    Chapter 2 Signal Acquisition Preprocessing and Feature Extraction Techniques for Biomedical Signals
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    Chapter 3 The Role of EEG as Neuro-Markers for Patients with Depression: A Systematic Review
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    Chapter 4 Brain-Computer Interface (BCI) Based on the EEG Signal Decomposition Butterfly Optimization and Machine Learning
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    Chapter 5 Advances in the Analysis of Electrocardiogram in Context of Mass Screening: Technological Trends and Application of AI Anomaly Detection
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    Chapter 6 Application of Wavelet Decomposition and Ma-Chine Learning for the sEMG Signal Based Ges-Ture Recognition
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    Chapter 7 Review of EEG Signals Classification Using Machine Learning and Deep-Learning Techniques
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    Chapter 8 Biomedical Signal Processing and Artificial Intelligence in EOG Signals
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    Chapter 9 Peak Spectrogram and Convolutional Neural Network-Based Segmentation and Classification for Phonocardiogram Signals
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    Chapter 10 Eczema Skin Lesions Segmentation Using Deep Neural Network (U-Net)
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    Chapter 11 Biomedical Signal Processing for Automated Detection of Sleep Arousals Based on Multi-Physiological Signals with Ensemble Learning Methods
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    Chapter 12 Deep Learning Assisted Biofeedback
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    Chapter 13 Estimations of Emotional Synchronization Indices for Brain Regions Using Electroencephalogram Signal Analysis
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    Chapter 14 Recognition Enhancement of Dementia Patients’ Working Memory Using Entropy-Based Features and Local Tangent Space Alignment Algorithm
Attention for Chapter 12: Deep Learning Assisted Biofeedback
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