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

Foreword
Preface
Acknowledgements
Contents
Contributors
Chapter 1: Introduction to Non-Invasive Biomedical Signals for Healthcare
1.1 Introduction to Biomedical Signals
1.2 Invasive and Non-Invasive Procedures
1.3 Non-Invasive Biomedical Signals
1.3.1 Electroencephalography (EEG)
1.3.2 Magnetoencephalography (MEG)
1.3.3 Electromyography (EMG)
1.3.4 Electrocardiography (ECG)
1.3.5 Electrooculography (EOG)
1.3.6 Phonocardiogram (PCG)
1.3.7 Photoplethysmography (PPG)
1.3.8 Magnetic Resonance Imaging (MRI)
1.4 Biomedical Signal Processing
1.4.1 Signal Acquisition
1.4.2 Signal Visualization and Annotation
1.4.3 Artifacts Removal and Preprocessing
1.4.4 Feature Extraction
1.5 Machine Learning in Biomedical Signal Analysis
1.6 Brain-Computer Interface BCI)
1.7 Neurofeedback & Biofeedback Systems
1.8 Conclusion
1.9 Teaching Assignments
References
Chapter 2: Signal Acquisition Preprocessing and Feature Extraction Techniques for Biomedical Signals
2.1 Introduction
2.2 The Biomedical Signal Acquisition and Processing
2.2.1 The Analog to Digital Conversion
2.2.2 The Digital Filtering
2.2.3 The Windowing
2.3 The Features Extraction Techniques
2.3.1 The Spectral Analysis
2.3.1.1 The Fourier Transform (FT)
2.3.1.2 The Parametric Model Based Methods
2.3.1.3 The Subspace Based Methods
2.3.2 The Time-Frequency Analysis
2.3.2.1 The Short-Time Fourier Transform
2.3.2.2 The Wavelet Transform
2.3.2.3 The Empirical Wavelet Analysis
2.3.2.4 The Empirical Mode Decomposition
2.4 Conclusion
2.5 Assignments for Readers
References
Chapter 3: The Role of EEG as Neuro-Markers for Patients with Depression: A Systematic Review
3.1 Introduction
3.2 Brain Structure and Depression
3.2.1 Brain Structure
3.2.2 Depression Types
3.2.2.1 Major Depression Disorder (MDD)
3.2.2.2 Premenstrual Dysphoric Disorder (PMDD(
3.2.2.3 Psychotic Depression
3.2.2.4 Postpartum Depression (PPD)
3.2.2.5 Persistent Depressive Disorder (PDD)
3.2.2.6 Seasonal Affective Disorder (SAD)
3.2.3 Effect of Depression on the Brain
3.3 Depression Diagnosis
3.3.1 Biomarkers
3.3.2 Psychological Assessments
3.3.3 Physiological Measurements
3.4 EEG-Based Depression Recognition Neuromarker
3.4.1 EEG and the Brain
3.4.2 Experimental EEG Protocol for Recognizing Depression
3.4.3 EEG Publicly Available Dataset for Depression Diagnosis
3.4.4 Function of EEG in Depression Detection and Classification
3.4.4.1 EEG Signal Acquisition Stage
3.4.4.2 Preprocessing Stage
3.4.4.3 Features Extraction Stage
3.4.4.3.1 Linear Spectral Features
3.4.4.3.2 Nonlinear Features
3.4.4.4 Dimensionality Reduction Stage
3.4.4.5 Depression Classification Techniques
3.5 Discussion
3.6 Conclusion
References
Chapter 4: Brain-Computer Interface (BCI) Based on the EEG Signal Decomposition Butterfly Optimization and Machine Learning
4.1 Introduction
4.2 The Evolution of BCI
4.3 Studies on the BCI
4.4 Methodology
4.4.1 Brain Waves Acquisition
4.4.2 Analog Amplifier and Filter
4.4.3 Analog to Digital (A/D) Conversion
4.4.4 Signal Conditioning
4.4.5 Features Extraction
4.4.6 Dimension Reduction
4.4.7 Classification
4.4.7.1 Support Vector Machine Classifier (SVM)
4.4.7.2 k-Nearest Neighbors (k-NN)
4.4.7.3 Artificial Neural Network (ANN)
4.4.8 Evaluation Measures
4.4.8.1 Accuracy
4.4.8.2 Precision
4.4.8.3 Recall
4.4.8.4 Specificity
4.4.8.5 F-Measure
4.4.8.6 Kappa
4.5 Results and Discussion
4.6 Conclusion
4.7 Assignments for Readers
References
Chapter 5: Advances in the Analysis of Electrocardiogram in Context of Mass Screening: Technological Trends and Application of AI Anomaly Detection
5.1 Introduction
5.2 Evolution of Views on the Role of the Electrocardiogram in Assessing the Risk of Major Adverse Cardiovascular Events
5.3 The Systems of Electrocardiographic Leads, Electrocardiogram with Limited Number of Leads for Heart Disease Screening
5.4 The Generations of ECG Analysis, some Modern Approaches Based on Mathematical Transformation of ECG Signal
5.5 Anomaly Detection in ECG Using Machine Learning Approach
5.6 Isolation Forest Anomaly Detection for Quantifying the Deviation of Signal Averaged ECG from Population Norm
5.6.1 Isolation Forest Unsupervised Anomaly Detection
5.6.2 Subjects Data
5.6.3 Quantification of the Distance to the Norm
5.6.4 Experiment Results
5.7 Teaching Assignment
5.8 Conclusion
References
Chapter 6: Application of Wavelet Decomposition and Ma-Chine Learning for the sEMG Signal Based Ges-Ture Recognition
6.1 Introduction
6.2 Literature Review
6.2.1 Background
6.2.2 Preprocessing for sEMG Based Gesture Recognition
6.2.3 Feature Selection Techniques for sEMG Based Gesture Recognition
6.2.4 Machine Learning and Deep Learning Techniques for sEMG Based Gesture Recognition
6.3 Methodology
6.3.1 Dataset
6.3.2 Machine Learning Algorithms
6.3.2.1 Support Vector Machine Classifier (SVM)
6.3.2.2 K-Nearest Neighbor (KNN)
6.3.3 Evaluation Measures
6.3.3.1 Accuracy
6.3.3.2 Precision
6.3.3.3 Specificity
6.3.3.4 Recall
6.3.3.5 F-Score
6.3.3.6 Kappa Statistics
6.4 Results and Discussion
6.5 Conclusion
6.6 Assignments for Readers
References
Chapter 7: Review of EEG Signals Classification Using Machine Learning and Deep-Learning Techniques
7.1 Introduction
7.2 Signal Pre-Processing
7.3 Features Extraction
7.3.1 Fast Fourier Transform (FFT)
7.3.2 Short-Time Fourier Transform (STFT)
7.3.3 Continuous Wavelet Transform
7.3.4 Discrete Wavelet Transform
7.3.5 Wavelet Packet Decomposition (WPD)
7.4 Features Selection
7.5 Machine Learning Techniques
7.6 Deep Learning Techniques
7.7 Case Studies
7.7.1 Epilepsy Detection
7.7.1.1 Dataset
7.7.1.2 Methodology
7.7.2 Schizophrenia Detection
7.7.2.1 Dataset
7.7.2.2 Methodology
7.8 Discussion
7.9 Conclusion
7.10 Assignments
References
Chapter 8: Biomedical Signal Processing and Artificial Intelligence in EOG Signals
8.1 Introduction
8.1.1 EOG Fundamentals
8.1.2 EOG Signal Measurement
8.2 EOG Signal Denoising
8.3 Compression
8.4 EOG Feature Processing
8.4.1 Feature Extraction
8.4.1.1 Time-Domain Features
8.4.1.2 Frequency Domain Features
8.4.1.3 Time-Frequency Features
8.4.1.4 Non-Linear Features
8.4.2 Feature Selection
8.4.3 Feature Normalization
8.5 Classification
8.5.1 Machine Learning Techniques
8.5.2 Deep Learning Techniques
8.6 Decision-Making
8.6.1 Intelligent Decision Support Systems
8.6.2 Learning Approaches
8.7 Discussion
8.8 Conclusions
References
Chapter 9: Peak Spectrogram and Convolutional Neural Network-Based Segmentation and Classification for Phonocardiogram Signals
9.1 Introduction
9.1.1 Auscultation
9.1.2 Phonocardiogram Signal
9.1.3 PCG Signal Acquisition
9.2 Related Work
9.2.1 Segmentation
9.2.2 Extracted Features and Classifiers
9.2.3 Unsegmented PCG Classification
9.3 Quality Assessment and Pre-processing of PCG Signals
9.3.1 Evaluation Criteria
9.3.2 Filtering and Spike Removal
9.4 Single and Multi-Level Threshold-Based Peak Detection Methods
9.5 Segmentation Methods of PCG Signals
9.5.1 Segmentation Based on Statistical Features and Support Vector Machine
9.5.2 Segmentation Based on Spectrograms and Convolutional Neural Network
9.6 Post-processing and Classification of PCG Signals
9.6.1 Post-processing and PCG Labeling
9.6.2 PCG Classification
9.7 Experimentation on the PhysioNet2016 Challenge Dataset
9.7.1 Dataset
9.7.2 Results of Pre-processing
9.7.3 Results of Segmentation
9.7.4 Results of Post-processing
9.7.5 Results of PCG Segmentation
9.8 Comparison Analysis and Discussions
9.9 Conclusions
References
Chapter 10: Eczema Skin Lesions Segmentation Using Deep Neural Network (U-Net)
10.1 Introduction
10.1.1 Eczema Area and Severity Index Measurement
10.1.2 Segmentation
10.2 Deep Learning Approach in Segmentation
10.2.1 Neural Network
10.2.2 Convolutional Neural Network (CNN)
10.2.3 Region-Based CNN (R-CNN)
10.2.4 Fully Convolutional Network (FCN)
10.2.5 Summary of Lesion Segmentation Literature
10.3 Methodology
10.3.1 Image Acquisition and Ground Truth Preparation
10.3.2 Image Pre-processing
10.3.2.1 Data Structure Preparation for Supervised Learning Methods
10.3.2.2 Adaptive Light Compensation (ALC)
10.3.2.3 Color Model Conversion
10.3.3 Data Augmentation
10.3.4 Image Segmentation
10.3.4.1 U-Net Architecture
10.3.4.2 U-Net Implementation
10.3.5 Image Post Processing
10.3.6 Segmentation Performance Analysis
10.4 Results and Discussion
10.4.1 Image Pre-processing for Ground Truth Images
10.4.2 Image Segmentation
10.4.2.1 Color Channels
10.4.2.2 Adaptive Light Compensation Technique (ALC)
10.4.3 Post-processing
10.4.4 Analysis of the Effect of Varying Kernel Number in Convolution Layer
10.4.5 Analysis of the Effect of Varying Steps per Epoch
10.4.6 Analysis of the Effect of Varying Number of Epochs
10.4.7 Comparison of Machine Learning and Deep Learning Methods
10.5 Conclusions
References
Chapter 11: Biomedical Signal Processing for Automated Detection of Sleep Arousals Based on Multi-Physiological Signals with Ensemble Learning Methods
11.1 Introduction
11.2 Polysomnography
11.2.1 EEG
11.2.1.1 Special Patterns in EEG
11.3 Sleep Stage
11.4 Methodology
11.4.1 Ensemble Learning
11.4.1.1 Bootstrap Aggregation (Bagging)
11.4.1.1.1 Random Forest (RF)
11.4.1.2 Boosting
11.4.1.2.1 Gradient Boosting Decision Tree (GBDT)
11.4.1.2.2 Light Gradient Boosting Machine (LightGBM)
11.4.2 Evaluating Performance
11.4.3 Data Description
11.4.4 Pre-Processing
11.4.4.1 EEG and EOG Signals
11.4.4.2 EMG Signal
11.4.4.3 ECG Signal
11.4.4.4 Airflow Signal
11.4.4.5 Signal Segmentation
11.4.4.6 Labeling Epochs
11.4.5 Feature Extraction
11.4.5.1 Features Extracted from EEG Signals
11.4.5.1.1 Frequency Features
11.4.5.1.2 Time-Frequency Features
11.4.5.1.3 Nonlinear Features
11.4.5.2 Features Extracted from EMG
11.4.5.3 Features Extracted from SaO2
11.4.5.4 Features Extracted from Airflow
11.4.5.5 Features Extracted from ECG
11.4.6 Data Balancing
11.4.7 Feature Selection
11.5 Classification Result
11.6 Conclusion
References
Chapter 12: Deep Learning Assisted Biofeedback
12.1 Introduction
12.2 Current Biofeedback and Neurofeedback Devices and Practice
12.3 Deep Learning Models for Electroencephalography Analysis
12.4 Deep Learning Assisted Biofeedback (DLAB)
12.4.1 PP-net: EEG Online Preprocessing
12.4.2 Sel-net: Classifying “Targeting” Signals for Feedback
12.4.3 Control-net: Extracting and Classifying for Feedback
12.4.4 FB-net: EEG Online Feedback
12.4.5 Config-net: Predictive Maintenance and Feedback Modulation
12.4.6 iClean-net: Cleaning Performance Perturbances
12.4.7 PN-net: Feedback Quality Control System and Interactive Database
12.4.8 Assess-net: Feedback Modulation Control Database
12.4.9 E-net
12.5 Discussion
12.6 Conclusions
References
Chapter 13: Estimations of Emotional Synchronization Indices for Brain Regions Using Electroencephalogram Signal Analysis
13.1 Introduction
13.2 Related Works
13.3 Materials and Methods
13.3.1 Subjects and Experimental Procedure
13.3.2 Preprocessing Stage
13.3.2.1 Conventional Filtering
13.3.2.2 Empirical Mode Decomposition with Wavelet (EMD − WT) Hybrid Denoising Technique
13.3.3 Features Extraction Stage
13.3.3.1 Linear Features
13.3.3.2 Nonlinear Features
13.3.4 Features Selection Using Statistical Analysis
13.3.5 Emotion Classification Stage
13.4 Results and Discussions
13.4.1 Results of Preprocessing Stage
13.4.2 Results of Features Extraction Stage
13.4.2.1 Results of Linear Features
13.4.2.2 Results of Nonlinear Features
13.4.3 Results of Features Selection and Emotion Classification Stages
13.4.3.1 Classification Results of Linear Features
13.4.3.2 Classification Results of Nonlinear Features
13.5 Conclusion
References
Chapter 14: Recognition Enhancement of Dementia Patients’ Working Memory Using Entropy-Based Features and Local Tangent Space Alignment Algorithm
14.1 Introduction
14.2 Related Works
14.3 Methods and Materials
14.3.1 Participants and EEG Recording
14.3.2 Preprocessing Stage
14.3.2.1 Conventional Filters
14.3.2.2 AICA–WT Technique Methodology
14.3.3 Features Extraction
14.3.3.1 Fuzzy Entropy (FuzzEn)
14.3.3.2 Fluctuation-Based Dispersion Entropy (FDispEn)
14.3.3.3 Bubble Entropy (BubbEn)
14.3.4 Statistical Analysis
14.3.5 Preliminary Feature Processing Prior Classification
14.3.6 Local Tangent Space Alignment (LTSA)
14.3.7 Dementia Classification Techniques
14.4 Results and Discussion
14.4.1 Results of Preprocessing Stage
14.4.2 Results of Dementia Recognition by Statistical Analysis
14.4.3 Results of Dementia Recognition by Classification and Performance Measure
14.5 Conclusion
References