Difference between revisions of "Compression of iEEG Data"
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===Literature=== | ===Literature=== | ||
* [https://pubmed.ncbi.nlm.nih.gov/29040672/] M. Baud, et al., Unsupervised Learning of Spatiotemporal Interictal Discharges in Focal Epilepsy, 2018 | * [https://pubmed.ncbi.nlm.nih.gov/29040672/] M. Baud, et al., Unsupervised Learning of Spatiotemporal Interictal Discharges in Focal Epilepsy, 2018 | ||
− | * [https://ieeexplore.ieee.org/document/8450511] A. Al-Marridi, Convolutional Autoencoder Approach for EEG Compression and Reconstruction in m-Health Systems, 2018 | + | * [https://ieeexplore.ieee.org/document/8450511] A. Al-Marridi, et al., Convolutional Autoencoder Approach for EEG Compression and Reconstruction in m-Health Systems, 2018 |
* [http://ieeg-swez.ethz.ch/] iEEG-SWEZ data base | * [http://ieeg-swez.ethz.ch/] iEEG-SWEZ data base | ||
Revision as of 16:58, 22 June 2020
Contents
Description
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) [1]. 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 [2]. 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 [3] consisting of a total of 2656 hours iEEG recordings.
Status: Available
Looking for student for Master's thesis or Semester project.
- Supervision: Michael Hersche, Xiaying Wang
Prerequisites
- Machine Learning
- Python & C Programming
Character
- 20% Theory
- 80% Programming
Professor
Literature
- [1] M. Baud, et al., Unsupervised Learning of Spatiotemporal Interictal Discharges in Focal Epilepsy, 2018
- [2] A. Al-Marridi, et al., Convolutional Autoencoder Approach for EEG Compression and Reconstruction in m-Health Systems, 2018
- [3] iEEG-SWEZ data base
Practical Details