About this Research Topic
Sleep consumes between 7 and 9 hours of our daytime, with children needs being even more demanding. Developing efficient automated analytical tools is thus a must to comprehensively study the physiological data generated along these hours in order to have a better understanding of sleep and its disorders.
Spectral and non-linear analyses have proven to be useful when analyzing overnight recordings. More recently, machine-learning and deep-learning algorithms have helped to objectivize decision-making in this context. Likewise, possible biomedical signals to be analyzed during sleep are electroencephalogram, electrocardiogram, blood oxygen saturation, or airflow, among others. Finally, imaging studies of the brain and upper airway, or digital imaging techniques (3D, thermal, NIRS, etc.) have also been shown to have great potential for the sleep research.
This Research Topic aims to collect articles uncovering new knowledge about normal and altered sleep physiology through the application of automated approaches or novel technologies that provide insights into sleep function and its disorders. The objects of study are as wide as sleep investigation itself. We here point to several unsolved research questions in the field:
- What are the relationships between sleep and cognition?
- How strong is the connection between sleep and the development of neurological and psychiatric diseases like Alzheimer’s or Schizophrenia?
- Can sleep apnea diagnosis be efficiently simplified?
- Can prediction of sleep disorders and their response to therapy be achieved using imaging techniques?
- How is the cardio-respiratory coupling affected in the presence of sleep pathologies?
- Does information brain circulation or blood brain barrier inform on sleep pathologies?
- Can sleep diseases change the normal brain’s connectivity pattern? Do these changes characterize specific sleep diseases?
Spectral and non-linear analyses have proven to be useful when analyzing overnight recordings. More recently, machine-learning and deep-learning algorithms have helped to objectivize decision-making in this context. Likewise, possible biomedical signals to be analyzed during sleep are electroencephalogram, electrocardiogram, blood oxygen saturation, or airflow, among others. Finally, imaging studies of the brain and upper airway, or digital imaging techniques (3D, thermal, NIRS, etc.) have also been shown to have great potential for the sleep research.
This Research Topic aims to collect articles uncovering new knowledge about normal and altered sleep physiology through the application of automated approaches or novel technologies that provide insights into sleep function and its disorders. The objects of study are as wide as sleep investigation itself. We here point to several unsolved research questions in the field:
- What are the relationships between sleep and cognition?
- How strong is the connection between sleep and the development of neurological and psychiatric diseases like Alzheimer’s or Schizophrenia?
- Can sleep apnea diagnosis be efficiently simplified?
- Can prediction of sleep disorders and their response to therapy be achieved using imaging techniques?
- How is the cardio-respiratory coupling affected in the presence of sleep pathologies?
- Does information brain circulation or blood brain barrier inform on sleep pathologies?
- Can sleep diseases change the normal brain’s connectivity pattern? Do these changes characterize specific sleep diseases?
Keywords: Sleep, automated analysis, biomedical signal processing, machine learning, medical imaging
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