KLASIFIKASI ABNORMALITAS ATRIAL FIBRILATION PADA SINYAL ECG MENGGUNAKAN DEEP LEARNING

SETIADI, RAIHAN MUFID and Fachrurrozi, Muhammad and Rachmatullah, Muhammad Naufal (2022) KLASIFIKASI ABNORMALITAS ATRIAL FIBRILATION PADA SINYAL ECG MENGGUNAKAN DEEP LEARNING. Undergraduate thesis, Sriwijaya University.

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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 abnormalities 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 abnormalities 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. However, in testing the unseen model data, it only achieved F1 Score value of 35% on the AFIB label classification and 17% on the Normal label. Keywords: Atrial Fibrillation, CNN 1D, Normal, Non-Atrial Fibrillation, ECG Signal

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	CNN 1 Dimensi, Atrial Fibrillation, ECG, Eletrocardiograph, Deep Learning
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.B45 Big data. Machine learning. Quantitative research. Metaheuristics. Q Science > QA Mathematics > QA8.9-QA10.3 Computer science. Artificial intelligence. Computational complexity. Data structures (Computer scienc. Mathematical Logic and Formal Languages
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Raihan Mufid Setiadi
Date Deposited:	16 Sep 2022 05:14
Last Modified:	16 Sep 2022 05:14
URI:	http://repository.unsri.ac.id/id/eprint/78905

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