Sleep-disordered breathing, ranging from habitual snoring to severe obstructive sleep apnea, is highly prevalent and represents a growing global healthcare burden. Besides disrupting sleep, sleep-disordered breathing leads to detrimental outcomes such as excessive daytime sleepiness, neurocognitive impairment, and increased cardiometabolic morbidities. The gold standard diagnostic procedures (e.g., in-lab polysomnography) are cumbersome and standard parameters derived from sleep studies often yield poor long-term prognostic value. Hence, there is a need for innovative diagnostic technologies and novel sleep metrics to enable simple diagnosis and tailored disease management.
This Research Topic aims to provide an overview of the recent technical developments in sleep medicine focusing on sleep-disordered breathing diagnosis and management.
1. The diagnostic approaches encompass both the technological developments to enhance the diagnostic recordings making them more accurate, efficient, and even suitable for home environments as well as novel computational solutions to automatize the analysis of the recordings and to gain more information from the recorded biosignals.
2. The disease management approaches encompass the physiological modeling, phenotyping, and endotyping approaches for better patient characterization including disease severity, daytime symptoms, as well as comorbidity conditions.
We are interested in original works, protocols, literature reviews, and meta-analyses related to sleep disorders with a specific focus on sleep-disordered breathing.
Possible topics include, but are not limited to:
Diagnosis:
1. New insights into physiological signals collected from standard sleep studies;
2. Novel sensor technology;
3. AI-based signal analysis approach;
4. Consumer devices
5. Big data approaches in sleep medicine
Management:
1. Phenotyping and endotyping approaches to better understand both the observable characteristics and the underlying reasons for disease progression and individual differences
2. Clustering approaches for big data, especially multi-center studies focusing on treatment outcomes
3. Tailored and individualized treatment
4. Telemonitoring
5. Emerging technologies to provide alternative treatment options for better treatment adherence and clinical outcomes.
Sleep-disordered breathing, ranging from habitual snoring to severe obstructive sleep apnea, is highly prevalent and represents a growing global healthcare burden. Besides disrupting sleep, sleep-disordered breathing leads to detrimental outcomes such as excessive daytime sleepiness, neurocognitive impairment, and increased cardiometabolic morbidities. The gold standard diagnostic procedures (e.g., in-lab polysomnography) are cumbersome and standard parameters derived from sleep studies often yield poor long-term prognostic value. Hence, there is a need for innovative diagnostic technologies and novel sleep metrics to enable simple diagnosis and tailored disease management.
This Research Topic aims to provide an overview of the recent technical developments in sleep medicine focusing on sleep-disordered breathing diagnosis and management.
1. The diagnostic approaches encompass both the technological developments to enhance the diagnostic recordings making them more accurate, efficient, and even suitable for home environments as well as novel computational solutions to automatize the analysis of the recordings and to gain more information from the recorded biosignals.
2. The disease management approaches encompass the physiological modeling, phenotyping, and endotyping approaches for better patient characterization including disease severity, daytime symptoms, as well as comorbidity conditions.
We are interested in original works, protocols, literature reviews, and meta-analyses related to sleep disorders with a specific focus on sleep-disordered breathing.
Possible topics include, but are not limited to:
Diagnosis:
1. New insights into physiological signals collected from standard sleep studies;
2. Novel sensor technology;
3. AI-based signal analysis approach;
4. Consumer devices
5. Big data approaches in sleep medicine
Management:
1. Phenotyping and endotyping approaches to better understand both the observable characteristics and the underlying reasons for disease progression and individual differences
2. Clustering approaches for big data, especially multi-center studies focusing on treatment outcomes
3. Tailored and individualized treatment
4. Telemonitoring
5. Emerging technologies to provide alternative treatment options for better treatment adherence and clinical outcomes.
