Real-Time Detection of Restlessness Caused by Rapid Eye Movement Sleep Behaviour Disorder

Bachelor thesis (2023)

Authors

J.R. Post Electrical Engineering, Mathematics and Computer Science
K. Kandiyoor Electrical Engineering, Mathematics and Computer Science
P. Kremers Electrical Engineering, Mathematics and Computer Science

Contributors

P.J. French Bio-Electronics - EEMCS (mentor)
L. Abelmann Bio-Electronics - EEMCS (coach)
Thomas Bakker (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Jasper Post, Krishnan Kandiyoor, Pepijn Kremers
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Published Date

23-06-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

The objective of this project was to develop a real-time restlessness detection algorithm for Rapid Eye Movement Sleep Behavior Disorder (RBD). This project was completed in collaboration with Momo Medical, a fast-growing start-up, and developer of the BedSense device. Our project aimed to design a system capable of detecting RBD episodes and bringing patients to a lighter sleep stage. To accomplish this, proprietary data and existing data from an RBD patient were collected through the BedSense device. Additionally, data was extracted using a hardware system (developed by another subgroup) from a non-RBD test subject. The collected data was utilized to design and implement a restlessness detection algorithm. Given the limited data collected for this project, a classical detection algorithm was developed instead of machine learning techniques. The software system demonstrated impressive performance, achieving a balanced accuracy of 95.94%, an average sensitivity of 100%, and an average specificity of 91.88%.
The results of this project were integrated with another project seeking to develop a sensor system that brings an RBD patient to a lighter sleep stage. This project built a sock system with embedded sensors and a vibration module to achieve this. The proof of concept of the final integrated system demonstrated a non-invasive method of detecting and preventing episodes caused by RBD.