Agenda
MSc CE Thesis Presentation
- Friday, 23 February 2018
- 16:00-16:40
- EWI HB 17.150
Energy Efficient Feature Extraction for Single-Lead ECG Classification Based On Spiking Neural Networks
Eralp KolagasiogluCardiovascular diseases are the leading cause of death in the developed world. Preventing these deaths, require long term monitoring and manual inspection of ECG signals, which is a very time consuming process. Consequently, a wearable system that can automatically categorize beats is essential.
Neuromorphic machines have been introduced relatively recently in the science community. The aim of these machines is to emulate the brain. Their low power design makes them an optimal choice for a low power wearable ECG classifier.
As features are crucial in any machine learning system, this thesis aims at proposing an energy efficient feature extraction algorithm for ECG arrhythmia classification using neuromorphic machines. The feature extraction algorithm proposed in this thesis consists of the merger of a low power feature detection and a feature selection algorithm. Also, different network configurations have been investigated to achieve classification using an LSM architecture. The resulting system can accurately cluster seven beat types, has an overall classification rate of 95.5%, and consumes an estimate of 803.62 nW.
Agenda
- Wed, 20 Mar 2024
- 13:30
- HB17.140 (Seminar room)
MSc SPS Thesis presentation
Ankush Roy
Sparse Non-uniform Optical Phased Array Design
- Thu, 21 Mar 2024
- 11:00
- HB 17.140
Signal Processing Seminar
Costas Pelekanakis
- Thu, 21 Mar 2024
- 16:00
- HB17.140 (Seminar room)
MSc SPS Thesis presentation
Kunlei Yu
Sparse Non-uniform Optical Phased Array Design
- Mon, 6 May 2024
- 12:30
- Aula Senaatszaal
PhD Thesis Defence
Christoph Manss
Multi-agent exploration under sparsity constraints
- 27 -- 28 May 2024
- Aula, TU Delft
Conferences
44th Benelux Symposium on Information Theory and Signal Processing (SITB'24, Delft)
- Tue, 18 Jun 2024
- 15:00
- Aula Senaatszaal
PhD Thesis Defence
Hanie Moghaddasi
Model-based feature engineering of atrial fibrillation
- Mon, 24 Jun 2024
- Aula, TU Delft