دانلود کتاب Advances in the Diagnosis and Treatment of Sleep Apnea: Filling the Gap Between Physicians and Engineers

فهرست مطالب :

Preface References Contents Part I: Physiology 1: An Overview on Sleep Medicine 1.1 The Origin and Regulation of Sleep 1.2 Sleep Impairment 1.3 Sleep Medicine 1.4 Future Directions References 2: Covering the Gap Between Sleep and Cognition – Mechanisms and Clinical Examples 2.1 Why We Need to Sleep? 2.2 Sleep Electrophysiology 2.2.1 Acquisition of the Electroencephalogram 2.2.2 Sleep Stages and the Cyclical Sleep 2.2.3 The Nested Hierarchy of Electrophysiological Waves during Sleep 2.3 Memory Consolidation – The Role of Sleep Spindles 2.4 Is There Room for Slow Oscillations? 2.5 Consequences of Poor Sleep Quality – Illustrative Examples 2.5.1 Non-pathological or Quasi-Pathological Consequences 2.5.2 Sleep Apnea and Cognitive Consequences 2.5.3 Migraine and Sleep – A Bidirectional Relationship? 2.5.4 The Role of Glymphatic System and Sleep Spindles in Alzheimer’s Disease 2.5.4.1 Sleep Spindles as Biomarker of Schizophrenia 2.6 Conclusion References 3: Obstructive Sleep Apnoea: Focus on Pathophysiology 3.1 Introduction 3.2 Pharyngeal Pressure 3.2.1 Craniofacial Morphology 3.2.2 Soft Tissue Accumulation 3.2.3 Fluid Accumulation 3.2.4 Nasal Obstruction 3.2.5 Other Factors Influencing Upper Airway Calibre 3.3 Upper Airway Dilator Muscle Function 3.4 Respiratory Control 3.4.1 Apnoea Threshold 3.5 Sleep Effects 3.5.1 Loop Gain 3.5.2 Arousal 3.6 Pathophysiological Endotypes and Phenotypes 3.7 Integrated Pathophysiology 3.8 Implications for Treatment 3.9 Conclusion References 4: Diagnosis of Obstructive Sleep Apnea in Patients with Associated Comorbidity 4.1 Introduction 4.2 Chronic Obstructive Pulmonary Disease (COPD) 4.3 Cardiovascular Diseases 4.3.1 Atrial Fibrillation 4.3.2 Chronic Ischemic Heart Disease 4.3.3 Chronic Heart Failure 4.4 Cerebrovascular Diseases 4.5 Diabetes References 5: Pediatric Obstructive Sleep Apnea: What’s in a Name? 5.1 Historical Perspective and Epidemiology 5.2 Risk Factors 5.3 Anatomic Considerations 5.4 Upper Airway Anatomy 5.4.1 Nasal Passages 5.4.2 Pharynx 5.4.3 Soft Tissues: Tonsils and Adenoids 5.4.4 Functional Considerations Underlying OSA in Children 5.4.5 Ventilatory Drive 5.4.6 Inspiratory Resistive Loading 5.4.7 Arousals from Sleep 5.4.8 Neuromotor Tone 5.4.9 Special Population: Childhood Obesity 5.5 Clinical Presentation 5.5.1 History 5.5.2 Physical Examination 5.5.3 Differential Diagnosis 5.6 Diagnosis 5.6.1 AASM Scoring Guidelines 5.7 Alternatives to PSG 5.7.1 Sleep Clinical Record (SCR) 5.7.2 Nocturnal Oximetry 5.7.3 Polygraphy 5.7.4 Portable Studies References 6: Treatment of Cheyne-Stokes Respiration in Heart Failure with Adaptive Servo-Ventilation: An Integrative Model 6.1 Introduction 6.2 Methods 6.2.1 “In Silico Subjects” 6.2.2 Computer Model 6.2.3 ASV 6.2.4 “Protocol” for Model Simulations and Subsequent Analyses 6.3 Results 6.3.1 Illustration of ASV Effect on Respiratory, Cardiovascular, and Autonomic Variables 6.3.2 Comprehensive Summary of ASV Effect on Ventricular Function and Respiratory Stability 6.4 Discussion 6.4.1 Is CSR the Consequence of or Compensatory Mechanism to CHF? 6.4.2 The Impact of ASV on CHF-CSR Includes Restoring Stable Breathing and Elevating Intrathoracic Pressure 6.4.3 ASV Significantly Reduces Coronary Flow 6.4.4 ASV Further Alters Sympathovagal Balance That Is Already Abnormal in CHF-CSR 6.4.5 What Could Explain the Higher Mortality Among CHF-CSR with Low EF Treated with ASV? 6.5 Limitations 6.6 Conclusion References Part II: Diagnostic Innovations 7: Automated Scoring of Sleep and Associated Events 7.1 Development of Autoscoring Systems: From Simple Decision Trees to Deep Neural Network Classifiers 7.1.1 Problem Statement 7.1.2 Autoscoring According to Rechtschaffen and Kales 7.1.3 Autoscoring According to AASM 7.1.4 Machine Learning Approaches 7.2 Validation of an Artificial Intelligence-Based Autoscoring System for PSGs 7.2.1 Methods 7.2.1.1 PSG Identification and Scoring 7.2.1.2 Statistical Power 7.2.1.3 Statistical Analyses 7.2.2 Results 7.2.2.1 Sleep Staging 7.2.2.2 Respiratory Events 7.2.2.3 Arousals 7.2.2.4 Periodic Limb Movements 7.2.3 Discussion 7.3 Added Value of Autoscoring Systems: From the Hypnodensity to Confidence Trends 7.3.1 Scoring in Real Time 7.3.2 Scoring According to Different Rules 7.3.3 Scoring with Different Sensitivity Settings 7.3.4 Estimating Sleep Stage Probabilities per Epoch (Hypnodensity) 7.3.5 Estimating Signal Quality 7.3.6 Identification of Periods with Clinically Relevant Ambiguities (Confidence Trends) 7.3.7 Visualization of Sleep/Wake-Related Features 7.3.8 Cardiorespiratory Sleep Staging for Home Sleep Apnea Testing (HSAT) 7.4 Future Directions References 8: Conventional Machine Learning Methods Applied to the Automatic Diagnosis of Sleep Apnea 8.1 Introduction 8.2 Data Analyzed in the Simplification of Sleep Apnea Diagnosis 8.2.1 Typical Overnight Biomedical Signals 8.2.1.1 Airflow (AF) 8.2.1.2 Blood Oxygen Saturation (SpO2) 8.2.1.3 Electrocardiogram and Heart Rate Variability (ECG/HRV) 8.2.2 Other Sources of Information 8.2.3 Important Databases 8.2.3.1 Sleep Heart Health Study (SHHS) 8.2.3.2 Childhood Adenotonsillectomy Trial (CHAT) 8.3 Methods: Classic Machine Learning Approaches in Sleep Apnea Diagnosis 8.3.1 Classification 8.3.1.1 Binary Classification 8.3.1.2 Multiclass Classification 8.3.2 Regression 8.3.3 Machine Learning Performance Assessment and Validation 8.3.3.1 Underfitting and Overfitting 8.3.3.2 Validation Strategy 8.3.3.3 Performance Statistics 8.4 Selected Results from the Literature 8.5 Discussion and Conclusions References 9: Home Sleep Testing of Sleep Apnea 9.1 Introduction 9.2 Classification of Methods for the Monitoring of Sleep Apnea at Home and in the Lab 9.3 Home Sleep Apnea Testing (HSAT) with Type 3 Portable Monitors 9.3.1 HSAT Utilizing Flow and/or Effort Parameters 9.3.2 HSAT Utilizing Peripheral Arterial Tonometry (PAT) 9.4 Motivation and Indication for Use of Simplified HSAT Devices for Sleep Apnea 9.5 Measurement Techniques Used for Simplified HSAT 9.5.1 Oximetry and Pulse Wave Analysis 9.5.2 Nasal Flow 9.5.3 ECG Measures 9.5.4 Transthoracic Impedance (TTI) 9.6 Surrogates of Respiration Gained by Minimal-Contact and Contactless Techniques 9.6.1 Sound Analyses 9.6.2 Movement Analyses 9.7 Conclusion Literature 10: ECG and Heart Rate Variability in Sleep-Related Breathing Disorders 10.1 Introduction 10.2 Rationale and Scientific Basis of HRV in SDB 10.3 HRV Measurements 10.3.1 Time-Domain Heart Rate Variability Analysis 10.3.2 Frequency-Domain Heart Rate Variability Analysis 10.3.2.1 Conventional Frequency-Domain Analysis 10.3.2.2 Bispectral Analysis 10.3.2.3 Wavelet Analysis 10.3.3 Nonlinear Analysis 10.3.3.1 Detrended Fluctuation Analysis 10.3.3.2 Entropy Analysis 10.3.3.3 Symbolic Dynamics 10.3.3.4 Poincaré Plots 10.3.3.5 Recurrence Plots 10.3.3.6 Chaotic Invariant Analysis 10.4 Future Research Direction References 11: Cardiopulmonary Coupling 11.1 Introduction 11.2 Physiological Basics 11.3 Analytical Methods for Cardiopulmonary Coupling 11.4 Distinct Patterns of Cardiopulmonary Coupling and Its Association with CAP and PSG 11.5 Sleep Stability Is Independent of Continuous Sleep Depth 11.6 Clinical Application of Cardiopulmonary Coupling Technique 11.6.1 Diagnosis of Sleep Apnea 11.6.2 Distinguishing Sleep Apnea Types 11.6.3 Treatment Tracking in Sleep Apnea 11.7 Cardiopulmonary Coupling Spectrogram in Other Disorders 11.7.1 Insomnia/Mental Health 11.7.2 Cardio-Cerebral Metabolic Health 11.8 Conclusion 11.9 Clinical Practice Points 11.10 Research Points References 12: Pulse Oximetry: The Working Principle, Signal Formation, and Applications 12.1 Working Principle 12.1.1 Green, Red, and Infrared Light 12.2 Photoplethysmogram 12.2.1 Blood Oxygen Saturation 12.2.2 Pulse 12.3 Error Sources and Limitations 12.4 Applications 12.4.1 Consumer Use 12.4.2 Clinical Use References 13: Oximetry Indices in the Management of Sleep Apnea: From Overnight Minimum Saturation to the Novel Hypoxemia Measures 13.1 Introduction 13.2 Approaches for Parameterizing Changes in the Dynamics of the Oximetry Signal 13.2.1 Conventional Approaches to Characterize the Overnight Oximetry Profile: Visual Inspection, Common Statistics, and the Oxygen Desaturation Index 13.2.1.1 An Especial Oximetric Index in Childhood OSA: Clusters of Desaturations 13.2.2 Analysis of Nocturnal Oximetry in the Frequency Domain 13.2.3 Methods Derived from Nonlinear Dynamics in the Oximetry Signal 13.2.4 Quantifying the Morphology of Desaturation: Influence of the Area and the Velocity of Events 13.2.5 Oximetry and Deep Learning Approaches 13.3 Discussion and Conclusions References 14: Airflow Analysis in the Context of Sleep Apnea 14.1 Introduction 14.2 Analysis in Time Domain 14.3 Analysis in Frequency Domain 14.4 Time–Frequency Analysis 14.5 Other Combined Approaches 14.6 Discussion 14.6.1 AF Characterization in Adults 14.6.2 AF Characterization in Children 14.7 Conclusions References 15: Deep-Learning Model Based on Convolutional Neural Networks to Classify Apnea–Hypopnea Events from the Oximetry Signal 15.1 Introduction 15.2 Materials and Methods 15.2.1 Subjects and Signals 15.2.2 Proposed CNN Architecture 15.2.3 CNN Training Process 15.2.4 Statistical Analysis 15.3 Results 15.3.1 CNN Model Performance 15.3.2 Estimation of Respiratory Indices 15.4 Discussion 15.5 Conclusions References 16: Tracheal Sound Analysis 16.1 Introduction 16.2 Tracheal Sounds 16.3 Tracheal Sound Sensors 16.4 Tracheal Sound Technology: A Reliable Recording for SDB Diagnosis 16.4.1 Time Domain Analysis: TS for Classical Manual Detection of Apneas and Hypopneas 16.4.2 Frequency Domain Analysis: TS Spectral Analysis for Automatic Detection of Apneas and Hypopneas 16.5 Respiratory Event Characterization 16.5.1 Respiratory Effort Evaluation: The Gold Standard and Real-Life Practice 16.5.2 Suprasternal Pressure: A TS Signal for Respiratory Effort Evaluation 16.5.3 Choking Noise Detection: A TS Noise for Apnea Characterization 16.6 Combination of TS with Other Sensors 16.6.1 Tracheal Sounds and RIP Belts for a “Sensor-Face-Free” Sleep Recording 16.6.2 Nasal Pressure and TS for the Detection of Oral Breathing 16.7 Tracheal Sounds Beyond the Usual Respiratory Information 16.7.1 Catathrenia: More Than Just a Regular Snoring 16.7.2 Tracheal Sound Energy Ratio: An Advanced Analysis for Upper Airway Resistance Evaluation 16.7.3 Cardiogenic Oscillations: TS for Heart Rate Variability 16.7.4 Detection of Obstruction Sites: Could TS Be an Alternative to DISE? 16.8 Conclusion References 17: Obstructive Sleep Apnea with COVID-19 17.1 Introduction 17.2 Influence of OSA on Incidence, Disease Severity, and Mortality in COVID-19 17.3 Putative Mechanistic Pathways Underlying the Impact of COVID-19 Infection on OSA 17.4 OSA Diagnosis During the COVID-19 Pandemic 17.5 Treatment of OSA During the COVID-19 Pandemic 17.6 Outcomes in Patients with OSA and COVID-19 Infection 17.7 Recommendations on the Management of Patients with OSA During the COVID-19 Pandemic 17.7.1 Diagnostic Management 17.7.2 Therapeutic Management References Part III: Therapeutic Innovations 18: APAP, BPAP, CPAP, and New Modes of Positive Airway Pressure Therapy 18.1 Introduction 18.2 Technology to Control Positive Airway Pressure Devices 18.2.1 Flow Generators 18.2.2 Flow Signal Processing 18.2.3 Respiratory Cycle Determination 18.2.4 Pressure Control 18.2.5 Leak Compensation 18.2.6 Apnea and Hypopnea Determination 18.2.7 Differentiating Between Obstructive and Central Apneas 18.2.8 Flow Limitation and Snore Determination 18.2.9 Mask and Humidification and Sound Technology 18.3 Positive Airway Pressure Modes and Algorithms to Control the Flow of Air 18.3.1 Continuous Positive Airway Pressure (CPAP) 18.3.2 Autotitrating Continuous Positive Airway Pressure (APAP) 18.3.3 Clinical Considerations Related to APAP Technology 18.3.4 Ramp and Starting Pressure Adjustments 18.3.5 Expiratory Pressure Relief Systems 18.3.6 Bilevel PAP (BPAP) 18.3.7 Respiratory Control Settings: Rise Time, Trigger and Cycle Sensitivity, and Inspiration Time 18.3.8 Clinical Considerations Related to BPAP Technology 18.3.9 BPAP Expiratory Pressure Relief 18.3.9.1 AutoBPAP 18.3.10 Adaptive or Anticyclic Servoventilation (SV) 18.3.11 Clinical Considerations with SV 18.3.12 Volume-Assured Pressure Support 18.3.13 Clinical Considerations with VAPS 18.4 Research Agenda 18.5 Conclusion References 19: Adherence Monitoring Using Telemonitoring Techniques 19.1 Background 19.2 Recent Advances 19.3 Discussion 19.4 Conclusion References 20: Innovations in the Treatment of Pediatric Obstructive Sleep Apnea 20.1 Importance of Sleep 20.2 Diagnosis of OSA 20.3 Overview of Treatment 20.3.1 Weight Management for Obesity 20.3.2 Anti-Inflammatory Therapy 20.3.3 Orthodontic Management 20.3.3.1 Rapid Maxillary Expansion 20.3.3.2 Mandibular Advancement 20.3.4 Surgical Treatment of Pediatric OSA 20.3.4.1 Drug Induced Sleep Endoscopy (DISE) 20.3.4.2 Nasal and Nasopharyngeal Surgery 20.3.4.3 Oropharyngeal Surgery 20.3.4.4 Tongue Surgery 20.3.4.5 Tracheotomy 20.3.5 Positive Airway Pressure (PAP) Therapy 20.3.6 Myofunctional Approaches References 21: Hypoglossal Nerve Stimulation Therapy 21.1 Introduction 21.2 Hypoglossal Nerve Stimulation Techniques 21.2.1 Unilateral Hypoglossal Nerve Stimulation Therapy with Respiratory Sensing 21.2.2 Unilateral Hypoglossal Nerve Stimulation Therapy Without Respiratory Sensing 21.2.3 Bilateral Hypoglossal Nerve Stimulation Therapy Without Respiratory Sensing 21.2.4 Noninvasive Electrical Stimulation 21.3 Study Situation on Hypoglossal Nerve Stimulation 21.3.1 Effects Following HNS Therapy 21.3.2 Sleep Architecture Changes 21.3.3 HNS with Down Syndrome 21.3.4 HNS and Cardiovascular Disease 21.3.5 HNS and Heart Rate Variability 21.3.6 HNS and Hypertension 21.3.7 HNS and Electrical Cardioversion 21.3.8 HNS with Cardiac Implantable Electronic Device 21.4 Patient Selection 21.4.1 Baseline Clinical Characteristics 21.4.2 Drug-Induced Sleep Endoscopy 21.4.3 Sleep Lab Testing 21.4.3.1 PSG 21.4.3.2 OSA Phenotyping 21.4.4 Clinical Anatomical/Radiographic Predictors 21.5 Surgical Procedure 21.5.1 Unilateral Hypoglossal Nerve Stimulation Therapy with Respiratory Sensing 21.5.2 Unilateral Hypoglossal Nerve Stimulation Therapy Without Respiratory Sensing 21.5.3 Bilateral Hypoglossal Nerve Stimulation Therapy Without Respiratory Sensing 21.5.4 HNS Therapy Versus Traditional Upper Airway Surgery 21.6 Postoperative Management and Care 21.6.1 Device Titration and Optimal Stimulus Parameters 21.6.2 Patients’ Adherence and Experience 21.6.3 Monitoring Methods 21.7 Complications and Adverse Events 21.7.1 Treatment-Emergent Central Sleep Apnea (TECSA) 21.7.2 Cheyne-Stokes Breathing 21.7.3 Adverse Events 21.7.4 Revision Surgery 21.8 Current Developments and Outlook for the Future 21.9 Conclusion References 22: Mandibular Advancement Splint Therapy 22.1 Introduction 22.2 Mechanism of Action 22.3 Efficacy and Adherence: MAS Versus CPAP 22.4 Patient Selection and Prediction of Response: Endotypes and Phenotypes 22.5 Health Outcomes 22.6 Neurobehavioural Outcomes 22.7 Quality of Life 22.8 Cardiovascular Outcomes 22.9 Design and Customisation 22.10 Adherence 22.11 MAS Titration 22.12 Side Effects 22.13 Patient-Centred Approach 22.14 Multidisciplinary Management 22.15 Future Directions 22.16 Clinical Practice Points: Evidence-Based Summary 22.17 Areas of Future Research References Index