KLASIFIKASI PENYAKIT JANTUNG ATRIAL FIBRILLATION (AF) DENGAN MENGGUNAKAN METODE RECURRENT NEURAL NETWORK (RNN) PADA KASUS MULTICLASS

KHOIRANI, RAHMI and Nurmaini, Siti (2019) KLASIFIKASI PENYAKIT JANTUNG ATRIAL FIBRILLATION (AF) DENGAN MENGGUNAKAN METODE RECURRENT NEURAL NETWORK (RNN) PADA KASUS MULTICLASS. Undergraduate thesis, Sriwijaya University.

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Abstract

Atrial Fibrillation (AF) is one of the most common heart diseases characterized by irregular heartbeat rhythms. AF can be detected via an electrocardiogram (ECG) signal. This study proposes one of the Deep Learning technique, namely the Recurrent Neural Network (RNN) with Long Short Term Memory (LSTM) architecture. This study classifies three classes of ECG signals, namely normal, atrial fibrillation, and others. The 2017 PhysioNet Computing in Cardiology (CinC) Challenge database is used. The pre-processing steps to classify the heart signal are bound normalization, denoising the signal with Discrete Wavelet Transform (DWT), data resampling with Random Oversampling (ROS), and segmentation with window size. Based on these stages, the results of the classification model with a window size of 900, and 16 iteration batch size. The distribution of training and testing data ratios of 90% and 10%, respectively. The results are obtained with accuracy, sensitivity, specificity, precision, and F-measure is 92.12%, 88.36%, 94.31%, 87.99%, and 87.89%, respectively. This research shows that RNN-LSTM can classify AF through ECG signals with the characteristic of sequential data.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi, Atrial Fibrillation, Elektrokardiogram, Recurrent Neural Network, Long Short Tem Memory, Random Oversampling
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Users 1953 not found.
Date Deposited:	19 Sep 2019 08:40
Last Modified:	19 Sep 2019 08:40
URI:	http://repository.unsri.ac.id/id/eprint/8136

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