Research Topic

Unraveling sleep and its disorders using novel analytical approaches

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?


Keywords: Sleep, automated analysis, biomedical signal processing, machine learning, medical imaging


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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?


Keywords: Sleep, automated analysis, biomedical signal processing, machine learning, medical imaging


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

08 January 2021 Abstract
08 July 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

08 January 2021 Abstract
08 July 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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