TELKOMNIKA Telecommunication, Computing, Electronics and Control
Unidirectional-bidirectional recurrent networks for cardiac disorders classification
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Unidirectional-bidirectional recurrent networks for cardiac disorders classification
Unidirectional-bidirectional recurrent networks for cardiac disorders classification
Subject
Bidirectional
Gated recurrent unit
Long short-term memory
Recurrent neural networks
Unidirectional
Gated recurrent unit
Long short-term memory
Recurrent neural networks
Unidirectional
Description
The deep learning approach of supervised recurrent network classifiers model,
i.e., recurrent neural networks (RNNs), long short-term memory (LSTM), and
gated recurrent units (GRUs) are used in this study. The unidirectional and
bidirectional for each cardiac disorder (CDs) class is also compared.
Comparing both phases is needed to figure out the optimum phase and the best
model performance for ECG using the Physionet dataset to classify five classes
of CDs with 15 leads ECG signals. The result shows that the bidirectional
RNNs method produces better results than the unidirectional method. In
contrast to RNNs, the unidirectional LSTM and GRU outperformed the
bidirectional phase. The best recurrent network classifier performance is
unidirectional GRU with average accuracy, sensitivity, specificity, precision,
and F1-score of 98.50%, 95.54%, 98.42%, 89.93%, 92.31%, respectively.
Overall, deep learning is a promising improved method for ECG classification.
i.e., recurrent neural networks (RNNs), long short-term memory (LSTM), and
gated recurrent units (GRUs) are used in this study. The unidirectional and
bidirectional for each cardiac disorder (CDs) class is also compared.
Comparing both phases is needed to figure out the optimum phase and the best
model performance for ECG using the Physionet dataset to classify five classes
of CDs with 15 leads ECG signals. The result shows that the bidirectional
RNNs method produces better results than the unidirectional method. In
contrast to RNNs, the unidirectional LSTM and GRU outperformed the
bidirectional phase. The best recurrent network classifier performance is
unidirectional GRU with average accuracy, sensitivity, specificity, precision,
and F1-score of 98.50%, 95.54%, 98.42%, 89.93%, 92.31%, respectively.
Overall, deep learning is a promising improved method for ECG classification.
Creator
Annisa Darmawahyuni, Siti Nurmaini, Muhammad Naufal Rachmatullah,
Firdaus Firdaus, Bambang Tutuko
Firdaus Firdaus, Bambang Tutuko
Source
http://journal.uad.ac.id/index.php/TELKOMNIKA
Date
Nov 25, 2020
Contributor
peri irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
Annisa Darmawahyuni, Siti Nurmaini, Muhammad Naufal Rachmatullah,
Firdaus Firdaus, Bambang Tutuko, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Unidirectional-bidirectional recurrent networks for cardiac disorders classification,” Repository Horizon University Indonesia, accessed November 22, 2024, https://repository.horizon.ac.id/items/show/3873.
Unidirectional-bidirectional recurrent networks for cardiac disorders classification,” Repository Horizon University Indonesia, accessed November 22, 2024, https://repository.horizon.ac.id/items/show/3873.