About this Research Topic
Sleep and circadian rhythm contain rich information about health, including but not limited to the nervous system, cardiovascular system, immune system, genetics and epigenetics, and their complex interactions.
In parallel, the occurrence of seizures in the sleep state is also observed in nearly one third of patients with epilepsy. The interplay of sleep and epilepsy is complex and poses a challenge for human to disentangle. However, the current practice standards in sleep, circadian rhythm, and epilepsy are only looking at the tip of the iceberg of all available information. On the other hand, data-driven approaches, represented by artificial intelligence (AI), offer promise to objectively, reproducibly, and accurately extract information, enabling more informed and precise treatment decisions, which ultimately improve patient outcomes.
Currently, the research problem is what are the biological or digital markers of sleep, circadian rhythm, and sleep-epilepsy interaction that we can extract, related to health outcomes, and guide treatment?
The goal is three-fold: (1) automate existing markers in sleep staging, apnea detection, delayed sleep-wake phase disorder, and spike and seizure detection; (2) extract new markers, such as brain aging, cognitive ability, indicators of narcolepsy, insomnia or depression, complexity measures of circadian rhythm, and sleep-epilepsy interaction; (3) ways to validate, use, and deploy these markers, and relate to the underlying mechanism, clinical care, and patient outcomes.
This Research Topic welcomes review papers and original research on the following themes but is not limited to them:
- Automated transferable sleep scoring algorithms such as sleep stages, arousals, respiratory events, limb movement, and snoring;
- Automated generalizable seizure and spike detection and sleep-based biomarkers to predict seizure or spikes and investigate their relationships;
- Novel markers derived from omics, polysomnography, brain imaging, home devices, actigraphy, or invasive devices (blood, cerebrospinal fluid, etc);
- Using such markers to gain knowledge of mechanisms, decision making, and precision medicine.
Professor Cathy Goldstein declares the following conflicts of interest:
- Huxley medical advisory board
- Sunrise consultant
- 5% inventor of app licensed to Arcascope LLC
The others topic editors declare no conflict of interest.
In parallel, the occurrence of seizures in the sleep state is also observed in nearly one third of patients with epilepsy. The interplay of sleep and epilepsy is complex and poses a challenge for human to disentangle. However, the current practice standards in sleep, circadian rhythm, and epilepsy are only looking at the tip of the iceberg of all available information. On the other hand, data-driven approaches, represented by artificial intelligence (AI), offer promise to objectively, reproducibly, and accurately extract information, enabling more informed and precise treatment decisions, which ultimately improve patient outcomes.
Currently, the research problem is what are the biological or digital markers of sleep, circadian rhythm, and sleep-epilepsy interaction that we can extract, related to health outcomes, and guide treatment?
The goal is three-fold: (1) automate existing markers in sleep staging, apnea detection, delayed sleep-wake phase disorder, and spike and seizure detection; (2) extract new markers, such as brain aging, cognitive ability, indicators of narcolepsy, insomnia or depression, complexity measures of circadian rhythm, and sleep-epilepsy interaction; (3) ways to validate, use, and deploy these markers, and relate to the underlying mechanism, clinical care, and patient outcomes.
This Research Topic welcomes review papers and original research on the following themes but is not limited to them:
- Automated transferable sleep scoring algorithms such as sleep stages, arousals, respiratory events, limb movement, and snoring;
- Automated generalizable seizure and spike detection and sleep-based biomarkers to predict seizure or spikes and investigate their relationships;
- Novel markers derived from omics, polysomnography, brain imaging, home devices, actigraphy, or invasive devices (blood, cerebrospinal fluid, etc);
- Using such markers to gain knowledge of mechanisms, decision making, and precision medicine.
Professor Cathy Goldstein declares the following conflicts of interest:
- Huxley medical advisory board
- Sunrise consultant
- 5% inventor of app licensed to Arcascope LLC
The others topic editors declare no conflict of interest.
Keywords: AI markers; sleep; circadian rhythm; epilepsy; biomarker; artificial intelligence
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.
