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KLASIFIKASI SINYAL ELEKTROKARDIOGRAM MENGGUNAKAN DENOISING AUTOENCODER DAN CONVOLUTIONAL NEURAL NETWORK

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Penilaian

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Arrhythmia is a sign or symptom of a heart rate or heart rhythm abnormality Early detection of arrhythmia can help patients in treating the disease appropriately. Arrhythmia disease can be detected using an electrocardiogram (ECG) which is a recording of electrical signals of cardiac activity. This study conducted a classification of normal cardiac and fibrillation arrhythmias on ECG signals. The Convolutional Neural Network method was proposed because it is able to process data that is non-linear in nature such as ECG signals. The data used are obtained from Physionet.org sites with an unbalanced distribution of classes containing noise. To overcome data containing noise, the Denoising Autoencoder method is used to remove noise from the ECG signal and autoencoder to extract features from the ECG signal that has been removed noise. The two techniques above showed the results of the accuracy value of 74.62%, sensitivity of 75.11%, specificity of 70%, precision of 95.93% and F1 Score of 84.26%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2207004007	T79617	T796172022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T796172022

Publisher

Inderalaya : Jurusan Sistem Komputer, Fakultas Ilmu Komputer., 2022

Collation

xiv, 64 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.680 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)

Local Area Network
Jurusan Sistem Komputer

Specific Detail Info

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Statement of Responsibility

SEPTA

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