Sleep-Energy: An Energy Optimization Method to Sleep Stage Scoring

• Published in: IEEE access 2023-01, Vol.11, p.1-1
• Main Authors: Aristimunha, Bruno, Bayerlein, Alexandre Janoni, Jorge Cardoso, M., Pinaya, Walter Hugo Lopez, De Camargo, Raphael Yokoingawa
• Format: Article
• Language: English
• Subjects: Automated Sleep Staging; Biomedical Engineering; Brain modeling; Computers and information processing; Datasets; Deep learning; Electroencephalogram; Electroencephalography; Energy Optimization; Hidden Markov models; Machine learning; Mental health; Optimization; Predictive models; Sleep; Systems, man, and cybernetics
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3263477

Sleep is essential for physical and mental health. Polysomnography (PSG) procedures are labour-intensive and time-consuming, making diagnosing sleep disorders difficult. Automatic sleep staging using ML-based methods has been studied extensively, but frequently provides noisier predictions incompatible with typical manually annotated hypnograms. We propose an energy optimization method to improve the quality of hypnograms generated by automatic sleep staging procedures. The method evaluates the system's total energy based on conditional probabilities for each epoch's stage and employs an energy minimisation procedure. It can be used as a meta-optimisation layer over the sleep stage sequences generated by any classifier that generates prediction probabilities. Results show that it improves the accuracy of the predictions of state-of-the-art Deep Learning models using two public datasets.