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

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