Non-Invasive Monitoring and Prevention of RBD Episodes

Bachelor thesis (2023)

Authors

K.B. Dzhumageldyev Electrical Engineering, Mathematics and Computer Science
J.H. Kruize Electrical Engineering, Mathematics and Computer Science
T.R.J. Schram Electrical Engineering, Mathematics and Computer Science

Contributors

P.J. French Bio-Electronics - EEMCS (supervisor 1)
T.D. Bakker (supervisor 1)
L. Abelmann Bio-Electronics - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Kerim Dzhumageldyev, Jan Kruize, Tijn Schram
More Info
expand_more

Published Date

23-06-2023

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

The goal of this project was to develop a sensor system that detects Rapid Eye Movement Sleep Behaviour Disorder (RBD) episodes in an early stage and brings the patient to a lighter sleep stage at the episode onset. This project was completed in close collaboration with Momo Medical, a fast-growing start-up and developer of the BedSense - a device placed under mattresses that tracks the bed posture and restlessness of residents in nursing homes. To detect RBD episodes, the BedSense was used in combination with a sock that houses the biosensors EDA, PPG and an accelerometer. The sock also contains a vibrator module, that can bring the patient to a lighter sleep stage in case of an episode.

The outcomes of this project are promising, data has been recorded from both non-RBD test subjects and an RBD patient, and the vibration module has been tested.

The results of this project were integrated with another project seeking to develop an algorithm to detect RBD episodes in an early stage. This project used data from the Momo BedSense and from the sock that has been created in this project.

Files

BAP_Thesis_K2.pdf
(.pdf | 7.07 Mb)