University of Houston's Bhavin R. Sheth and former student Adam Jones have developed a groundbreaking machine learning model.

The model classifies sleep-wake cycle stages based on neural oscillations.

It utilizes phase-amplitude couplings as input data to achieve high accuracy.

The model performs well even with limited training data.

This new approach offers an objective method for identifying sleep stages.

Potential applications include both research and clinical settings.

Collaboration with Laurent Itti at USC underscores the research's potential impact.

The innovation could improve sleep-related healthcare interventions and revolutionize sleep monitoring.