Difference between revisions of "Compression of iEEG Data"
|Line 1:||Line 1:|
[[Category:Digital]][[Category:Semester Thesis]][[Category:Available]] [[Category:2020]][[Category:Hot]][[Category:Human Intranet]][[Category:xiaywang
[[Category:Digital]][[Category:Semester Thesis]][[Category:Available]] [[Category:2020]][[Category:Hot]][[Category:Human Intranet]][[Category:xiaywang]][[Category:Epilepsy]]
Latest revision as of 09:28, 16 September 2021
Seizure detection systems hold promise for improving the quality of life for patients with epilepsy that afflicts nearly 1% of the world’s population. High-resolution intracranial Electroencephalography (iEEG) enables the detection and location of such seizures. When aiming a low power implanted system the large amount of data has to be efficiently reduced. iEEG signals are sparse and have been successfully compressed using well-established encoders such as Discrete Wavelet Transform (DWT) or Non-Negative Matrix Factorization (NNMF) . Due to its recent success, however, convolutional neural networks (CNNs) are getting more attention and have shown to be a viable option to compress EEG signals . This project compares deep convolutional autoencoders with state-of-the-art DWT and NNMF to compress iEEG data from long-term recordings of epileptic patients. We use the publicly available long-term dataset  consisting of a total of 2656 hours iEEG recordings.
Looking for student for Master's thesis or Semester project.
- Supervision: Xiaying Wang
- Machine Learning
- Python & C Programming
- 20% Theory
- 80% Programming
-  M. Baud, et al., Unsupervised Learning of Spatiotemporal Interictal Discharges in Focal Epilepsy, 2018
-  A. Al-Marridi, et al., Convolutional Autoencoder Approach for EEG Compression and Reconstruction in m-Health Systems, 2018
-  iEEG-SWEZ data base