MSc ME Thesis Presentation

Design Space Exploration of a Neuromorphic ECG Classification System using a Spiking Self-Organizing Map

Johan Mes

The Self-Organizing Map (SOM) is an unsupervised neural network topology that incorporates competitive learning for the classification of 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 SOM and its encapsulating system by exploring configuration parameters such 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 with the aim of being implemented using power efficient neuromorphic hardware.

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 & classification system is essential. Unsupervised and in realtime, this system performs beat detection with an average TPR of 99.10% and a PPV of 99.58% and classification of 500 detected beats with an EMDS of 0.0169 and a beat recognition percentage of 100%.

Overview of MSc ME Thesis Presentation