Print Email Facebook Twitter Design Space Exploration of a Neuromorphic ECG Classification System using a Spiking Self-Organizing Map Title Design Space Exploration of a Neuromorphic ECG Classification System using a Spiking Self-Organizing Map Author Mes, Johan (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Signal Processing Systems) Contributor van Leuken, T.G.R.M. (mentor) Kumar, S.S. (graduation committee) Zjajo, Amir (graduation committee) Al-Ars, Z. (graduation committee) Degree granting institution Delft University of Technology Programme Electrical Engineering | Microelectronics Date 2018-09-28 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%. Subject snnsommachine learningneuromorphicneuronsynapsestdpecgclassificationneuromorphic machine To reference this document use: http://resolver.tudelft.nl/uuid:4d9bcec6-d5e6-4ced-876c-addcd7198c98 Embargo date 2023-10-01 Part of collection Student theses Document type master thesis Rights © 2018 Johan Mes Files PDF MasterThesis_JohanMes_417 ... ersion.pdf 3.78 MB Close viewer /islandora/object/uuid:4d9bcec6-d5e6-4ced-876c-addcd7198c98/datastream/OBJ/view