Automated Movement Detection with Dirichlet Process Mixture Models and Electromyography

TL;DR

This paper introduces an automated limb-movement detection framework using Dirichlet process mixture models on EMG data, outperforming traditional methods and aiding clinical diagnosis of sleep movement disorders.

Contribution

It presents a novel approach combining Dirichlet process mixture models with EMG features for automated movement detection in sleep studies.

Findings

Achieved 94% accuracy in movement detection

Outperformed random forest classifier in sensitivity and specificity

Demonstrated potential for clinical application in sleep disorder diagnosis

Abstract

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.