firdaus, firdaus (2021) similiarity_Unidirectional-bidirectional recurrent networks for cardiac disorders classification. Turnitin Universitas Sriwijaya.
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Abstract
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. © 2021. This is an open access article under the CC BY-SA license.
| Item Type: | Other |
|---|---|
| Subjects: | #3 Repository of Lecturer Academic Credit Systems (TPAK) > Results of Ithenticate Plagiarism and Similarity Checker |
| Divisions: | 09-Faculty of Computer Science > 56201-Computer Systems (S1) |
| Depositing User: | Mr Firdaus Firdaus |
| Date Deposited: | 17 Mar 2023 13:35 |
| Last Modified: | 17 Mar 2023 13:35 |
| URI: | http://repository.unsri.ac.id/id/eprint/90935 |
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