Energy Efficient Feature Extraction for Single-Lead ECG Classification Based On Spiking Neural Networks

Master thesis (2018)

Authors

A.E. Kolağasioğlu Electrical Engineering, Mathematics and Computer Science

Contributors

Amir Zjajo (mentor)
T.G.R.M. van Leuken (graduation committee member)
S.S. Kumar (graduation committee member)
Z. Al-Ars (graduation committee member)
Carlo Galuzzi (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2018 Eralp Kolağasioğlu
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Published Date

23-02-2018

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

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.

Files

Thesis_Eralp_Kolagasioglu.pdf
(.pdf | 3.31 Mb)
- Embargo expired in 01-09-2018