Skripsi

DETEKSI ATRIAL FIBRILLATION MENGGUNAKAN RECURRENT NEURAL NETWORK PADA SINYAL ELEKTROKARDIOGRAM LONG TERM

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Penilaian

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Atrial fibrillation (AF) is a heart disease characterized by an irregular heartbeat. So it can cause stroke, heart failure and even death. AF disease is the most common and mostly affects the elderly. The thing that can be done to detect AF disease is by recording an electrocardiogram (ECG) signal. The ECG has electrical recording information of the heart's activity that describes the condition of the heart's health. In this study, one of the Deep Learning methods is used, namely themethod Recurrent Neural Network (RNN) with Long Short Term Memory (LSTM) architecture, because this method is very suitable for data related to sequential data such as ECG signals. This study classified 3 classes of ECG signals, namely normal, AF and non-AF signals on theparameters learning rate, epoch and batch size. And the results of the performance evaluation of the 3 classes of ECG signals obtained the best model with average values of accuracy, precision, sensitivity, specificity and F1 of 96.79%, 93.04%, 93.83%, 86.58%, and 89.48%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2107004084	T61652	T616522021	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T616522021

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Uniersitas Sriwijaya., 2021

Collation

xvii, 49 hlm,: ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.650 7

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Jaringan Komunikasi Komputer
Prodi Sistem Komputer

Specific Detail Info

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Statement of Responsibility

MURZ

Other version/related

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