Design Space Exploration of a Neuromorphic ECG Classification System using a Spiking Self-Organizing Map
J.C. Mes (TU Delft - Electrical Engineering, Mathematics and Computer Science)
T.G.R.M. van Leuken – Mentor
SS Kumar – Graduation committee member
A Zjajo – Graduation committee member
Z. Al-Ars – Graduation committee member
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
The Self-Organizing Map (SOM) is an unsupervised neural networktopology that incorporates competitive learning for the classicationof data. In this thesis we investigate the design space of a system incorporating such a topology based on Spiking Neural Networks (SNNs), and apply it to classifying electrocardiogram (ECG) beats. We present novel insights into the characterization of the SOMand its encapsulating system by exploring conguration parameterssuch as learning rate, neuron models, potentiation and depression ratios, and synaptic conductivity parameters by performing high-level architectural simulations of the system whose SNN is developed withthe aim of being implemented using power ecient neuromorphichardware. Due to the amount of manual work needed to monitor and analyze ECG signals when diagnosing cardiovascular problems, and because it is the leading cause of death in the world, an automated, realtime, and low power detection & classication system is essential. Unsupervised and in realtime, this system performs beat detection with an average True Positive Rate (TPR) of 99.10% and a Positive Predictive Value (PPV) of 99.58% and classication of 500 detected beats with a Multidimensional Scaling Error (EMDS) of 0.0169 and a beat recognition percentage of 100%.