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

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

doi:10.1109/CBMS.2018.00082

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

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

Signal Processing Systems

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-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 language	English
Title of host publication	2018 IEEE 31st IEEE International Symposium on Computer-Based Medical Systems(CBMS)
Editors	R. Bilof
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	434-435
Number of pages	2
ISBN (Electronic)	978-1-5386-6060-7
ISBN (Print)	978-1-5386-6061-4
DOIs	https://doi.org/10.1109/CBMS.2018.00082
Publication status	Published - 2018
Event	31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018 - Karlstad, Sweden Duration: 18 Jun 2018 → 21 Jun 2018

Conference

Conference	31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018
Country/Territory	Sweden
City	Karlstad
Period	18/06/18 → 21/06/18

Keywords

ECG data classification
neuromorphic network
noise
SNN
uncertainty

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10.1109/CBMS.2018.00082

Cite this

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title = "Uncertainty in Noise-Driven Steady-State Neuromorphic Network for ECG Data Classification",

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.",

keywords = "ECG data classification, neuromorphic network, noise, SNN, uncertainty",

author = "Amir Zjajo and Johan Mes and Sumeet Kumar and Eralp Kolagasioglu and {van Leuken}, Rene",

year = "2018",

doi = "10.1109/CBMS.2018.00082",

language = "English",

isbn = "978-1-5386-6061-4 ",

pages = "434--435",

editor = "R. Bilof",

booktitle = "2018 IEEE 31st IEEE International Symposium on Computer-Based Medical Systems(CBMS)",

publisher = "IEEE",

address = "United States",

note = "31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018 ; Conference date: 18-06-2018 Through 21-06-2018",

}

Zjajo, A, Mes, J, Kumar, S, Kolagasioglu, E & van Leuken, R 2018, Uncertainty in Noise-Driven Steady-State Neuromorphic Network for ECG Data Classification. in R Bilof (ed.), 2018 IEEE 31st IEEE International Symposium on Computer-Based Medical Systems(CBMS) . IEEE, Piscataway, NJ, pp. 434-435, 31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018, Karlstad, Sweden, 18/06/18. https://doi.org/10.1109/CBMS.2018.00082

Uncertainty in Noise-Driven Steady-State Neuromorphic Network for ECG Data Classification. / Zjajo, Amir; Mes, Johan; Kumar, Sumeet et al.
2018 IEEE 31st IEEE International Symposium on Computer-Based Medical Systems(CBMS) . ed. / R. Bilof. Piscataway, NJ: IEEE, 2018. p. 434-435.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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T1 - Uncertainty in Noise-Driven Steady-State Neuromorphic Network for ECG Data Classification

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

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Keywords

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