Thesis

1D Convolutional Neural Network to Detect Ventricular Fibrillation

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Ventricular fibrillation contributes to the majority of arrhythmia mortality and morbidity
rate, as studies show the survival rate of patients who have been discharged from the hospital
ranging from 3 to 33 percent while the mortality rate of patients who did not have fast access to
defibrillator exceeds 90 to 95 percent. The research aims to develop another method of ventricular
fibrillation detection. The research utilizes a convolutional neural network with ten-second ECG data
gathered from CU Ventricular Tachyarrhythmia Database to determine ventricular fibrillation
reading from normal reading. An accuracy of 90%, a sensitivity of 96%, and a specificity of 84% of
test data were obtained. The result is compared to the accuracy, sensitivity, and specificity of the
study conducted by Amann et al. (2005), Panda et al. (2020), and Sabut et al. (2021). The result did
not surpass the methods proposed by other studies as other studies use more datasets and have
tighter time intervals, although the model performance is quite enough for public use.

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Availability

Detail Information

Series Title

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Call Number

BI 22-006

Publisher

i3L, Jakarta : i3L, Jakarta., 2022

Collation

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Language

English

ISBN/ISSN

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Classification

NONE

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Ventricular Fibrillation
arrhythmia mortality
Morbidity Rate

Specific Detail Info

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Statement of Responsibility

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Other version/related

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