KLASIFIKASI ATRIAL FIBRILLATION PADA EKG DENGAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN)
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Institusion
Universitas Sriwijaya
Author
ANDRE HERVIANT JULIANO (STUDENT ID : 09011181520025)
Firdaus Firdaus (LECTURER ID : 0221017801)
Firdaus Firdaus (LECTURER ID : 0221017801)
Subject
TK7885-7895 Computer engineering. Computer hardware
Datestamp
2019-08-28 04:10:28
Abstract :
Electrocardiography plays a very important role in the medical field because it functions to evaluate electrical activity and conditions in the human heart. The results of the evaluation will be in the form of graphs or signals that represent the human heart rate per unit time or better known as the Electrocardiogram (ECG). Based on research that has been done in the last few years, deep learning has succeeded in classifying Atrial Fibrillation with a high degree of accuracy. The deep learning method proposed in this study is Convolutional Neural Networks (CNN). This is because CNN has the advantage of combining feature extraction based on feature learning and classification in a learning process. In the classification process based on ECG signals, 6 trial scenarios will be tested, each consisting of 7 convolution layers, 10 convolution layers, and 13 convolution layers with fully connected layers of 1000 nodes, 1000 nodes and 1 node for 2700 nodes signal and 18300 nodes signal with window size, and fully connected layer 100 nodes, 100 nodes and 1 node for 18300 nodes signal. From several experimental scenarios, the first experiment scenario using the 13 convolution layer model produced performance values for accuracy, precision, sensitivity, specificity, and f1 score respectively 92.97%, 77.78%, 87.46%, 87.46%, and 81.63%. The low precision, sensitivity and f1 scores of some CNN models are because some ECG signals have the same morphology between the normal signal and AF signal, as well as the amount of normal and unbalanced AF data.
Electrocardiography plays a very important role in the medical field because it functions to evaluate electrical activity and conditions in the human heart. The results of the evaluation will be in the form of graphs or signals that represent the human heart rate per unit time or better known as the Electrocardiogram (ECG). Based on research that has been done in the last few years, deep learning has succeeded in classifying Atrial Fibrillation with a high degree of accuracy. The deep learning method proposed in this study is Convolutional Neural Networks (CNN). This is because CNN has the advantage of combining feature extraction based on feature learning and classification in a learning process. In the classification process based on ECG signals, 6 trial scenarios will be tested, each consisting of 7 convolution layers, 10 convolution layers, and 13 convolution layers with fully connected layers of 1000 nodes, 1000 nodes and 1 node for 2700 nodes signal and 18300 nodes signal with window size, and fully connected layer 100 nodes, 100 nodes and 1 node for 18300 nodes signal. From several experimental scenarios, the first experiment scenario using the 13 convolution layer model produced performance values for accuracy, precision, sensitivity, specificity, and f1 score respectively 92.97%, 77.78%, 87.46%, 87.46%, and 81.63%. The low precision, sensitivity and f1 scores of some CNN models are because some ECG signals have the same morphology between the normal signal and AF signal, as well as the amount of normal and unbalanced AF data.
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RAMA_56201_09011181520025.pdf
RAMA_56201_09011181520025_TURNITIN.pdf
RAMA_56201_09011181520025_0221017801_01_front_ref.pdf
RAMA_56201_09011181520025_0221017801_02.pdf
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RAMA_56201_09011181520025_0221017801_05.pdf
RAMA_56201_09011181520025_0221017801_06_ref.pdf
RAMA_56201_09011181520025_0221017801_07_lamp.pdf
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