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

Amir Zjajo, Johan Mes, Sumeet Kumar, Eralp Kolagasioglu, Rene van Leuken

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2018 IEEE 31st IEEE International Symposium on Computer-Based Medical Systems(CBMS)
EditorsR. Bilof
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages434-435
Number of pages2
ISBN (Electronic)978-1-5386-6060-7
ISBN (Print)978-1-5386-6061-4
DOIs
Publication statusPublished - 2018
Event31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018 - Karlstad, Sweden
Duration: 18 Jun 201821 Jun 2018

Conference

Conference31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018
CountrySweden
CityKarlstad
Period18/06/1821/06/18

Keywords

  • ECG data classification
  • neuromorphic network
  • noise
  • SNN
  • uncertainty

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