Heerdink, Thomas (2023) Automatic detection of myoclonus bursts in EMG data. Bachelor's Thesis, Computing Science.
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Abstract
Myoclonus is a movement disorder characterised by brief, involuntary muscle contractions. Classifying myoclonus bursts by physiological origin can guide treatment options, but clinicians may find handling large datasets of EMG data acquired during these measurements challenging. This thesis addresses this issue by training a Long Short-Term Memory (LSTM) model to automatically detect myoclonus bursts in EMG data, paving the way for future burst classification projects. The findings suggest that the model is adept at correcting training set labels, underscoring the significant promise of employing machine learning algorithms for myoclonus burst detection.
| Item Type: | Thesis (Bachelor's Thesis) |
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| Supervisor name: | Biehl, M. and Bunte, K. |
| Degree programme: | Computing Science |
| Thesis type: | Bachelor's Thesis |
| Language: | English |
| Date Deposited: | 21 Aug 2023 10:40 |
| Last Modified: | 21 Aug 2023 10:40 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/31230 |
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