Skripsi

DETEKSI SINYAL ATRIAL FIBRILLATION PADA ELEKTROKARDIOGRAM MENGGUNAKAN CONVOLUTION NEURAL NETWORK 1-DIMENSI

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Penilaian

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Atrial fibrillation (AF) is one of the most common chronic heart diseases suffered by the elderly. AF is a potentially lethal arrhythmia that increases the risk of heart failure and stroke if not properly identified and diagnosed. Signs of AF are excessive upper atrial contraction, as well as P sine wave failure and significant irregularity of the QRS complex sequence on the electrocardiogram (ECG). Doctors need trained skills and technical knowledge to accurately explain the ECG and it also takes a long time to visually examine the ECG signal. Therefore, the development of algorithms for the automatic detection of atrial fibrillation is urgently needed. For accurate automatic detection, fast and reliable diagnosis is expected. The method used in this research is 1-Dimensional Convolution Neural Network. In this study, classification for 6 signal classes was carried out on the parameters of learning rate, batch size and epoch. The results of the performance evaluation of the classification of 6 classes of ECG signals with average values of accuracy, sensitivity, specificity, precision and F1 of 99.5%, 89.2%, 99.6%, 84.8% and 86.5%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2107002227	T52645	T526452021	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T526452021

Publisher

Inderalaya : Fakultas Ilmu komputer, Prodi Sistem Komputer., 2021

Collation

xiv, 89 hlm,: ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.707

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)

Prodi Sistem Komputer
Data Sistem Komputer

Specific Detail Info

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Statement of Responsibility

MURZ

Other version/related

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