This technology enables fully automatic sleep stage classification based on polysomnographic records using only two standard EEG signals.
The method applies cross-frequency coupling analysis to 30-second EEG intervals and uses machine learning to classify all five sleep stages with outstanding accuracy.
Applications
-The solution is designed for sleep diagnostics and the evaluation of polysomnographic data in clinical and research settings.
-It can help reduce manual workload, improve standardization and support more precise diagnosis and therapy recommendations for sleep disorders.
Technology stage
-The method is ready for use and has been tested with hundreds of datasets.
-In addition to the fully automated offline version, an expert-aided partially automated mode is also available for even higher accuracy or manual refinement.
