Deep neural networks for seizure detection
Epilepsy is one of the most prevalent chronic neurological disorders. One-third of patients with epilepsy continue to suffer from seizures despite pharmacological therapy. For these patients with drug-resistant epilepsy, efficient algorithms for seizure detection are needed in particular during pre-surgical monitoring. Many efforts have been pursued in this direction with the fabrication of many ASIC and the development of advanced machine/deep-learning to optimize both the energy efficiency for years-operating devices and the accuracy in the epilepsy detection. In terms of time-series analysis, a big variety of deep-learning approaches are arising for efficient processing such as InceptionTime , MultiScale-CNN , and Temporal Convolutional Networks (TCN) .
The thesis would be a 6-month full-time project with the following steps to accomplish:
1 - Development in a high-level programming language (python) of different deep learning algorithms for time series classification. In particular, the initial targets will be the InceptionTime, the MultiScale-CNN, the Temporal Convolutional Networks, and a bidirectional LSTM.
2 - Benchmarking of these algorithms on a large - scale dataset collected by the Bern Inelspital about epileptic patients .
3 - Comparison with state of the art methods (Local Binary pattern + Hyperdimensional computing , Short-time Fourier transform + Convolutional Neural Networks and classical machine learning methods).
4 - Characterization of the algorithm on different computing platforms, from the high-level number of operations to the number of cycles and energy consumption on embedded devices (e.g. GAP8, a Greenwaves multi-core chip).
Looking for one student for Master's thesis.
- Machine Learning
- Python & C Programming
- 20% Theory
- 80% Programming
- H. I. Fawaz et al., InceptionTime: Finding AlexNet for Time Series Classification 
- Z. Cui et al., Multi-Scale Convolutional Neural Networks for Time Series Classification 
- C. Lea et al., Temporal Convolutional Networks: A Unified Approach to Action Segmentation, 
- iEEG-SWEZ data base 
- A. Burello et al., Laelaps: An Energy-Efficient Seizure Detection Algorithm from Long-term Human iEEG Recordings without False Alarms