Abnormality Heartbeat Classification of ECG Signal Using Deep Neural Network and Autoencoder

firdaus, firdaus (2019) Abnormality Heartbeat Classification of ECG Signal Using Deep Neural Network and Autoencoder. Proceedings - 1st International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2019 (898520). pp. 213-218.

Text 32.pdf - Published Version Download (1MB)

Abstract

Electrocardiogram (ECG) is a device used by healthcare practitioners to monitor and processing of patient health data so can detect abnormality cardiovascular disease. Continuous heart supervision generates large amounts of data and analyzes this large data need classification method. This Paper exposes the classification of heartbeat abnormality based on the ECG signal by using Deep Neural Network (DNN). Three preprocessing stages of the ECG signal are applied before the classification process, which is segmentation, normalizing using normalize bound, and feature extraction by using Autoencoder. The results show that the applied method gets an outstanding accuracy about 99.22% and sensitivity about 98.03%. © 2019 IEEE.

Item Type:	Article
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:	Mr Firdaus Firdaus
Date Deposited:	17 Mar 2023 15:03
Last Modified:	17 Mar 2023 15:03
URI:	http://repository.unsri.ac.id/id/eprint/90716

Actions (login required)

View Item