Automated Movement Detection with Dirichlet Process Mixture Models and Electromyography

• Published in: 2022 25th International Conference on Information Fusion (FUSION) pp. 01 - 08
• Main Authors: Cooray, Navin, Li, Zhenglin, Wang, Jinzhuo, Lo, Christine, Arvaneh, Mahnaz, Symmonds, Mkael, Hu, Michele, De Vos, Maarten, Mihaylova, Lyudmila S
• Format: Conference Proceeding
• Language: English
• Published: International Society of Information Fusion 04-07-2022
• Subjects: Annotations; Dirichlet Process; Gaussian mixture model; Legged locomotion; Mixture models; movement detection; RBD; REM sleep behaviour disorder; Sensor fusion; Sensor phenomena and characterization; Training data; Visualization
• DOI: 10.23919/FUSION49751.2022.9841235

Numerous sleep disorders are characterised by movement during sleep, these include rapid-eye movement sleep behaviour disorder (RBD) and periodic limb movement disorder. The process of diagnosing movement related sleep disorders requires laborious and time-consuming visual analysis of sleep recordings. This process involves sleep clinicians visually inspecting electromyogram (EMG) signals to identify abnormal movements. The distribution of characteristics that represent movement can be diverse and varied, ranging from brief moments of tensing to violent outbursts. This study proposes a framework for automated limb-movement detection by fusing data from two EMG sensors (from the left and right limb) through a Dirichlet process mixture model. Several features are extracted from 10 second mini-epochs, where each mini-epoch has been classified as 'leg-movement' or 'no leg-movement' based on annotations of movement from sleep clinicians. The distributions of the features from each category can be estimated accurately using Gaussian mixture models with the Dirichlet process as a prior. The available dataset includes 36 participants that have all been diagnosed with RBD. The performance of this framework was evaluated by a 10-fold cross validation scheme (participant independent). The study was compared to a random forest model and outperformed it with a mean accuracy, sensitivity, and specificity of 94%, 48%, and 95%, respectively. These results demonstrate the ability of this framework to automate the detection of limb movement for the potential application of assisting clinical diagnosis and decision-making.