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Vagus nerve-based seizure detection algorithm analysis and ultra-low-power implementation

Hardy, Roman
(2024)

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Hardy_15501900_2024.pdf
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Details

Supervisors
Bol, David ; Nonclercq, Antoine
Faculty
Ecole polytechnique de Louvain
Degree label
Master [120] : ingénieur civil électricien, à finalité spécialisée
Abstract
According to the World Health Organization, more than 50 million peoples have epilepsy, making it one of the most common chronic brain diseases. As such, many medications to treat epilepsy are already available. Proper medication and diagnosis would make up to 70% of people with epilepsy seizure-free, mostly with antiepileptic drugs. However, as drugs are not always conclusive, research is conducted to improve them and bring new solutions. Amongst theses new ideas, a new type of closed-loop neurostimulator is investigated, using the vagus nerve to both predict the seizures and counter them. This work looks at the feasibility of a new algorithm to detect epileptic seizures based on the vagus nerve. The method was partially tested before, but never from an implementation point of view. This work will then put a strong focus on the challenges, possibilities and requirements coming from this new algorithm. To this end, the algorithm is implemented in C and characterized on an ultra-low-power microcontroller, allowing real measurements of power consumption and showing the memory limitations of an embedded device.

Citations

  • APA

Hardy, R. (2024). Vagus nerve-based seizure detection algorithm analysis and ultra-low-power implementation. https://dial-mem.test.bib.ucl.ac.be/handle/2078.2/39732