Automated Movement Detection with Dirichlet Process Mixture Models and Electromyography

• Main Authors: Cooray, Navin, Li, Zhenglin, Wang, Jinzhuo, Lo, Christine, Arvaneh, Mahnaz, Symmonds, Mkael, Hu, Michele, De Vos, Maarten, Mihaylova, Lyudmila S
• Format: Journal Article
• Language: English
• Published: 15-02-2023
• Subjects: Quantitative Biology - Quantitative Methods
• DOI: 10.48550/arxiv.2302.07509

2022 25th International Conference on Information Fusion (FUSION),
Link\"oping, Sweden, 2022, pp. 01-08 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.