Real-Time Detection of Restlessness Caused by Rapid Eye Movement Sleep Behaviour Disorder
J.R. Post (TU Delft - Electrical Engineering, Mathematics and Computer Science)
K. Kandiyoor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
P. Kremers (TU Delft - Electrical Engineering, Mathematics and Computer Science)
PJ French – Mentor (TU Delft - Bio-Electronics)
L. Abelmann – Coach (TU Delft - Bio-Electronics)
Thomas Bakker – Graduation committee member (Momo Medical B.V.)
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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.