Advance restless leg syndrome monitoring with deep learning
Yu, Hang
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https://hdl.handle.net/2142/127243
Description
Title
Advance restless leg syndrome monitoring with deep learning
Author(s)
Yu, Hang
Issue Date
2024-12-12
Director of Research (if dissertation) or Advisor (if thesis)
Wang, Yuxiong
Department of Study
Siebel School Comp & Data Sci
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Restless Leg Syndrome
Deep Learning
Transfer Learning
Language
eng
Abstract
Periodic Limb Movements (PLMs) are frequently observed in patients with Restless Legs Syndrome (RLS). While electromyography (EMG) of leg muscles is traditionally used to quantify the motor symptom burden of RLS, wearable trackers may cause discomfort to patients. This study investigates the efficacy of pressure-sensing mat data in detecting PLMs, offering a non-invasive alternative for monitoring limb movements during sleep. Our approach utilizes a pressure-sensing mat that captures subtle changes in pressure distribution, providing a comfortable method for continuous monitoring. We collected a comprehensive dataset comprising 153.5 hours of synchronized pressure mat and EMG recordings from 21 patients. Ground truth labels were derived from concurrent EMG data, ensuring reliable annotations for PLM detection. We also propose to finetune a deep learning model, 3D-ResNet18 with pretrained Kinetics700k weights, as a baseline for this dataset that predicts PLMs from pressure mat data. Our approach offers a comfortable alternative to EMG-based detection, opening new avenues for continuous home-based monitoring.
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