Healthcare Sciences SectionBiomedical Informatics
Predicting apnea during sleep to identify sleep apnea syndrome (SAS)
Sleep apnea syndrome (SAS) is highly prevalent but often goes undiagnosed and untreated. It can increase the risk of future health problems, as well as traffic accidents and reduced work performance. To help identify people who may have SAS, we are studying ways to predict apnea during sleep using wearable devices. In collaboration with companies, we aim to implement large-scale SAS screening as part of routine health checkups.


Faculty
Healthcare Sciences Section Biomedical Informatics Professor MORITA Mizuki
