IMPLEMENTASI SISTEM DETEKSI ATRIAL FIBRILASI BERBASIS KOMPUTASI AWAN MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK

RAMANSYAH, M. WAHYU and Nurmaini, Siti (2022) IMPLEMENTASI SISTEM DETEKSI ATRIAL FIBRILASI BERBASIS KOMPUTASI AWAN MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK. Undergraduate thesis, Sriwijaya University.

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Abstract

Atrial Fibrillation (AF) is a rhythm disorder in the heart where the consecutive irregular rhythm occurs due to atrial depolarization. Electrocardiogram (ECG) signals present the electrical activity of the heart. The ECG signals can be identified to classify varying the heart disease. In this study, deep learning based on a convolutional neural network algorithm is used to determine the condition of the heart, it can be extracted by the features. In addition, the process is not only to generate the deep learning model but also can be deployed. Hence, this study generates the design of a system-web based on cloud computing to detect the presence of heart disease, specifically AF. This study conducts three experiments using four public datasets. Among three experiments, the best model is the first experiment that used unseen data, and it was obtained the 96.40% accuracy, 94.75% sensitivity, 93.52% precision,93,52 specificity, and 94% F1 score. As the results, it can be concluded by using the cloud computing services, the best results show while using GPU devices with an inference time of 0.048 seconds, and a server throughput of 20 predictions per second.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Deep Learning, Convolutional Neural Network, Cloud Computing
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. Q Science > QA Mathematics > QA1-939 Mathematics > QA1.T553 Mathematics--Periodicals. Computer science--Periodicals. Computer science. R Medicine > R Medicine (General) > R856-857 Biomedical engineering. Electronics. Instrumentation > R857.M3.B56854 Biomedical materials. Stem cells--Therapeutic use. Regenerative medicine--Materials. TECHNOLOGY & ENGINEERING / Material Science. MEDICAL / Biotechnology T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	M. Wahyu Ramansyah
Date Deposited:	24 Jun 2022 02:59
Last Modified:	24 Jun 2022 02:59
URI:	http://repository.unsri.ac.id/id/eprint/72783

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