Fachrurrozi, Muhammad (2023) CLASSIFICATION OF ATRIAL FIBRILLATION IN ECG SIGNAL USING DEEP LEARNING. Sriwijaya Journal of Informatics and Applcations (SJIA), 4 (1). pp. 18-27. ISSN 2807-2391
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ARTIKEL - CLASSIFICATION OF ATRIAL FIBRILLATION.pdf - Accepted Version Download (1MB) |
Abstract
Atrial fibrillation is a type of heart rhythm disorder that most often occurs in the world and can cause death. Atrial fibrillation can be diagnosed by reading an Electrocardiograph (ECG) recording, however, an ECG reading takes a long time and requires specialists to analyze the type of signal pattern. The use of deep learning to classify Atrial Fibrillation in ECG signals was chosen because deep learning has 10% higher performance compared to machine learning methods. In this research, an application for classification of Atrial Fibrillation was developed using the 1-Dimentional Convolutional Neural Network (CNN 1D) method. There are 6 configurations of the 1D CNN model that were developed by varying the configuration on the learning rate and batch size. The best model obtained 100% accuracy, 100% precision, 100% recall, and 100% F1 Score.
| Item Type: | Article |
|---|---|
| Subjects: | Q Science > Q Science (General) > Q1-295 General |
| Divisions: | 09-Faculty of Computer Science > 55201-Informatics (S1) |
| Depositing User: | Dr. Muhammad Fachrurrozi |
| Date Deposited: | 05 Jul 2023 10:16 |
| Last Modified: | 05 Jul 2023 10:16 |
| URI: | http://repository.unsri.ac.id/id/eprint/114149 |
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