FM
fábio mendonça
University of Madeira
Funchal, Portugal
About this Research Topic
Manuscript Submission Deadline 23 March 2027
This Research Topic is currently accepting articles
Background
Sleep disorders are among the most prevalent health conditions worldwide, yet a large proportion of cases remain undiagnosed. The current diagnostic standard, in-laboratory polysomnography, is accurate but costly, time consuming, and limited in availability. As demand continues to grow, there is a clear need for diagnostic approaches that are faster, more scalable, and better suited to routine and remote use, while supporting care that is tailored to the individual.
Artificial intelligence is transforming how sleep is measured and understood. Automated analysis of physiological signals such as EEG, oximetry, and cardiorespiratory data now allows sleep disorders to be detected and characterized with increasing accuracy, often from simpler recordings than a full polysomnogram. Techniques for automatic sleep staging, cyclic alternating pattern analysis, and event detection are enabling a new generation of diagnostic tools that can operate beyond the traditional sleep laboratory, including in ambulatory and home settings. These developments bring precision sleep medicine closer to real-world practice.
This Research Topic focuses on the development, validation, and application of AI-driven diagnostic tools that advance personalized, precision-based sleep care. We are especially interested in tools that are rigorously validated and shown to perform reliably across specific clinical or at-risk groups, so that their benefits can be translated into practice. Contributions may span novel algorithms, robust validation studies, interpretability, and integration into clinical workflows, capturing the full path from signal to diagnosis.
We welcome contributions from across the field, including sleep researchers, clinicians, engineers, and data scientists. This collection seeks to strengthen both the science and the practical impact of intelligent diagnostic tools in sleep medicine.
Themes of interest include, but are not limited to:
• Machine learning for the detection and classification of sleep disorders
• Automated sleep staging and cyclic alternating pattern analysis
• Diagnosis and severity assessment of obstructive sleep apnea using reduced or wearable sensor sets
• Home-based and ambulatory sleep monitoring using low-cost or non-invasive technologies
• Analysis of EEG, oximetry (SpO2), and cardiorespiratory signals for sleep assessment
• Explainable and interpretable AI for clinical sleep applications
• Signal processing and feature engineering for physiological sleep data
• Validation of diagnostic tools across specific clinical or at-risk populations
• Integration of AI-driven diagnostic tools into personalized care pathways
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
- Brief Research Report
- Case Report
- Clinical Trial
- Community Case Study
- Conceptual Analysis
- Curriculum, Instruction, and Pedagogy
- Data Report
- Editorial
- FAIR² Data
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Keywords: Sleep medicine, Artificial intelligence, Machine learning, Deep learning, Sleep disorders, Obstructive sleep apnea, Automatic sleep staging, Cyclic alternating pattern, EEG analysis, Wearable sensors, Home sleep monitoring, Precision medicine
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.
