Predicting defibrillation outcome in ventricular fibrillation using ECG with neural network algorithm

Shang-Ho Tsai, P. Min-Shan Tsa, Hsin Chi Huang, Dean Chang Ash Ling

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This work proposes a system to predict the outcomes of the defibrillation during the period of ventricular fibrillation. Accurate outcomes can avoid inefficient defibrillation that causes severe myocardial injury. In this system, we apply a neural network model and use the frequency components of ECG signals as training data to determine the neuron coefficients. The trained system is then validated (tested) using different set of data to justify the performance. Experimental results are provided to show superior performance of the proposed system.

Original languageEnglish
Title of host publication2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728103976
DOIs
StatePublished - 1 Jan 2019
Event2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Sapporo, Japan
Duration: 26 May 201929 May 2019

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2019-May
ISSN (Print)0271-4310

Conference

Conference2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019
CountryJapan
CitySapporo
Period26/05/1929/05/19

Keywords

  • Amplitude spectrum area (AMSA)
  • Convolution neural network (CNN)
  • Defibrillation timing
  • Frequency variation (FY)
  • Learning
  • Neural network (NN)
  • Ventricular fibrillation (YF)

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  • Cite this

    Tsai, S-H., Min-Shan Tsa, P., Huang, H. C., & Ash Ling, D. C. (2019). Predicting defibrillation outcome in ventricular fibrillation using ECG with neural network algorithm. In 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings [8702288] (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCAS.2019.8702288