Description
- Abstract:
- This thesis aims to contribute to the practical deployment of a new type of multichannel subdermal wireless EEG system under development at the Nurmikko Lab for 24/7 longitudinal continuous long-term brain monitoring over months and possibly years, with emphasis on advancements in insertion techniques of the implant and data analysis, respectively. The focus of the thesis is on (a) a novel, minimally invasive method of implantation and (b) an innovative approach to managing large datasets anticipated to be generated by the EEG implant. The first contribution of this work is the development of a new insertion technique that employs bioresorbable polyethylene glycol (PEG) barbs. These barbs temporarily stiffen the EEG strip, allowing for easier and more precise placement under the skin while reducing the invasiveness of the procedure. Once inserted, the barbs dissolve harmlessly into the body, leaving the flexible EEG strip securely in place without additional intervention. This method is designed to diminish patient discomfort and simplifies the implantation process, making it a viable option for widespread clinical use. As the second constitution of this work, in anticipation of the vast amounts of EEG recorded data from long term continuous monitoring, the thesis introduces a robust feature importance extraction methodology that significantly enhances the computational efficiency of seizure detection algorithms. By identifying and focusing on the most impactful features, the machine learning model maintains high accuracy while reducing computation time by nearly 50%. This approach not only streamlines the processing of EEG data generated by a 24/7 chronically implanted device, but also provides a scalable data management solution adaptable to various long-term monitoring technologies. Together, these contributions address critical barriers in the deployment of minimally invasive, continuous monitoring systems, paving the way for their broader application in clinical settings. Future research will further refine these techniques and expand their application to other neurological monitoring scenarios.
- Notes:
- Thesis (Sc. M.)--Brown University, 2024
Citation
Chhabra, Akshit,
"Clinical Considerations for a Minimally Invasive Wireless EEG System"
(2024).
Biomedical Engineering Theses and Dissertations.
Brown Digital Repository. Brown University Library.
https://repository.library.brown.edu/studio/item/bdr:3duxhbn2/
Relations
Collection:
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Biomedical Engineering Theses and Dissertations
Theses and Dissertations for the Biomedical Engineering department....