Navin’s research was presented at the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in July 2018.
The article was published in IEEE Xplore on 29th October 2018. Read the full article on IEEE Xplore.
This study aims to develop automated diagnostic tools to aid in the identification of rapid-eye-movement (REM) sleep behaviour disorder (RBD). Those diagnosed with RBD enact their dreams and therefore present an abnormal characteristic of movement during REM sleep. Several methods have been proposed for RBD detection that use electromyogram (EMG) recordings and manually annotated sleep stages to objectively quantify abnormal REM movement. In this work we further develop these proven techniques with additional features that incorporate the relationship of muscle movement between sleep stages and general sleep architecture. Performance is evaluated using polysomnography (PSG) recordings from 43 aged-matched healthy controls and subjects diagnosed with RBD obtained from multiple institutions and publicly available resources. Using a random forest classifier with established and additional features, the performance of RBD detection was shown to improve upon established metrics (achieving 88% accuracy, 91% sensitivity, and 86% specificity).