Predicting Age with Deep Neural Networks from Polysomnograms
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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.
Original language | English |
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Title of host publication | 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society : Enabling Innovative Technologies for Global Healthcare, EMBC 2020 |
Number of pages | 4 |
Publisher | IEEE |
Publication date | 2020 |
Pages | 146-149 |
Article number | 9176254 |
ISBN (Electronic) | 9781728119908 |
DOIs | |
Publication status | Published - 2020 |
Event | 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Montreal, Canada Duration: 20 Jul 2020 → 24 Jul 2020 |
Conference
Conference | 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 |
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Land | Canada |
By | Montreal |
Periode | 20/07/2020 → 24/07/2020 |
Series | I E E E Engineering in Medicine and Biology Society. Conference Proceedings |
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ISSN | 2375-7477 |
ID: 262746552