Uncertainty in Noise-Driven Steady-State Neuromorphic Network for ECG Data Classification

The pathophysiological processes underlying the ECG tracing demonstrate significant heart rate and the morphological pattern variations, for different or in the same patient at diverse physical/temporal conditions. Within this framework, spiking neural networks (SNN) may be a compelling approach to ECG pattern classification based on the individual characteristics of each patient. In this paper, we study electrophysiological dynamics in the self-organizing map SNN when the coefficients of the neuronal connectivity matrix are random variables. We examine synchronicity and noise-induced information processing, influence of the uncertainty on the system signal-to-noise ratio, and impact on the clustering accuracy of cardiac arrhythmia.

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