Advance restless leg syndrome monitoring with deep learning

Yu, Hang

Permalink

https://hdl.handle.net/2142/127243 Copy

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

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.

Graduation Semester

2024-12

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/127243

Copyright and License Information

Copyright 2024 Hang Yu

Owning Collections