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KLASIFIKASI ABNORMALITAS ATRIAL FIBRILATION PADA SINYAL ECG MENGGUNAKAN DEEP LEARNING

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Penilaian

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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

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2207003675	T78905	T789052022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T789052022

Publisher

Inderalaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2022

Collation

xii, 116 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.650 7

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)

Jurusan Teknik Informatika
Jaringan Komunikasi Komputer

Specific Detail Info

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Statement of Responsibility

SEPTA

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