Print Email Facebook Twitter Energy Efficient Feature Extraction for Single-Lead ECG Classification Based On Spiking Neural Networks Title Energy Efficient Feature Extraction for Single-Lead ECG Classification Based On Spiking Neural Networks Author Kolağasioğlu, Eralp (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Microelectronics) Contributor Zjajo, Amir (mentor) van Leuken, T.G.R.M. (graduation committee) Kumar, S.S. (graduation committee) Al-Ars, Z. (graduation committee) Galuzzi, Carlo (graduation committee) Degree granting institution Delft University of Technology Programme Electrical Engineering | Circuits and Systems Date 2018-02-23 Abstract Cardiovascular diseases are the leading cause of death in the devel- oped world. Preventing these deaths, require long term monitoring and manual inspection of ECG signals, which is a very time consum- ing 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. Subject neuromorphic machinesspiking neural networksecg beat classificationfeature extractionlow powerglobal calssification To reference this document use: http://resolver.tudelft.nl/uuid:3b7de584-23c6-4d46-b32a-0fe57c4f8dbd Embargo date 2018-09-01 Part of collection Student theses Document type master thesis Rights © 2018 Eralp Kolağasioğlu Files PDF thesis_Eralp_Kolagasioglu.pdf 3.31 MB Close viewer /islandora/object/uuid:3b7de584-23c6-4d46-b32a-0fe57c4f8dbd/datastream/OBJ/view