Predicting Age with Deep Neural Networks from Polysomnograms

Andreas Brink-Kjaer, Emmanuel Mignot, Helge B D Sorensen, Poul Jennum


The aim of this study was to design a new deep learning framework for end-to-end processing of polysomnograms. This framework can be trained to analyze whole-night polysomnograms without the limitations of and bias towards clinical scoring guidelines. We validated the framework by predicting the age of subjects. We designed a hierarchical attention network architecture, which can be pre-trained to predict labels based on 5-minute epochs of data and fine-tuned to predict based on whole-night polysomnography recordings. The model was trained on 511 recordings from the Cleveland Family study and tested on 146 test subjects aged between 6 to 88 years. The proposed network achieved a mean absolute error of 7.36 years and a correlation to true age of 0.857. Sleep can be analyzed using our end-to-end deep learning framework, which we expect can generalize to learning other subject-specific labels such as sleep disorders. The difference in the predicted and chronological age is further proposed as an estimate of biological age.

TidsskriftProceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society
Sider (fra-til)146-149
Antal sider4
StatusUdgivet - jul. 2020


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