KLASIFIKASI SINYAL EKG MENGGUNAKAN DENOISING AUTOENCODER DAN DEEP NEURAL NETWORK

MUKTI, AKHMAD NOVIAR SATRIA and Nurmaini, Siti (2019) KLASIFIKASI SINYAL EKG MENGGUNAKAN DENOISING AUTOENCODER DAN DEEP NEURAL NETWORK. Undergraduate thesis, Sriwijaya University.

	Text RAMA_56201_09011381520052.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (3MB) | Request a copy
	Text RAMA_56201_09011381520052_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (14MB) | Request a copy
Preview	Text RAMA_56201_09011381520052_0002085908_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (808kB) | Preview
	Text RAMA_56201_09011381520052_0002085908_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (782kB) | Request a copy
	Text RAMA_56201_09011381520052_0002085908_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_56201_09011381520052_0002085908_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (2MB) | Request a copy
	Text RAMA_56201_09011381520052_0002085908_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (251kB) | Request a copy
	Text RAMA_56201_09011381520052_0002085908_06_ref.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (283kB) | Request a copy
	Text RAMA_56201_09011381520052_0002085908_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (8MB) | Request a copy

Abstract

Arrhythmia is a indication or a symtop of heart beat disruption or heart rhythm Early detection of arrhythmia could help patient in handling the abnormalities quickly. Arrhythmia can be detected using electrocardiogram (ECG), which is heart activity electrical signal recording. This research aims to classify normal heart, premature ventricular contraction, atrial premature beat, right bundle branch block beat and non-conducted p-wave on the ECG signal. Deep Neural Network is proposed due to the ability of processing non-linear data like ECG signal. The data used in this research is obtained from Physionet.org website with imbalanced class ratio and noise-contained. To overcome the noise-contained data, Denoising Autoencoder is proposed to denoise the signal and Autoencoder used to extract the feature of the denoised ECG signal. Both the technique above shows the results performance accuracy, sensitivity, specificity, precision and F1 Score is 99.06%, 93.56%, 99.35%, 89.42% and 91.11% repectively.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Electrocardiogram, Classify, Arrhythmia, Deep Neural Network, Denoising Autoencoder, Autoencoder
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Users 2015 not found.
Date Deposited:	24 Sep 2019 04:32
Last Modified:	24 Sep 2019 04:32
URI:	http://repository.unsri.ac.id/id/eprint/8617

Actions (login required)

View Item