Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Computer Networks, Architecture and High Performance Computing
Mardi Turnip
Information Systems Study Program, Faculty of Science and Technology, Prima Indonesia University, Medan
Computer Science & IT Education

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Application of Deep Learning for Cardiac Arrhythmia Classification Based on ECG Signals Gabriela Septiani Simbolon; Gresia Cesilia Sirait; Sarah Theresia Aruan; Rivaldo Robertus Turnip; Jepri Banjarnahor; Mardi Turnip
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 3 (2026): Research Paper July 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i3.8672

Abstract

Cardiac arrhythmia is a dangerous heart rhythm disorder, so early detection is crucial for effective treatment. Manual ECG (Electrocardiogram) analysis is less accurate, while deep learning can detect arrhythmias more quickly and precisely. The proposed algorithm uses a deep learning Convolutional Neural Network (CNN) model for arrhythmia classification. The model is trained on labeled normal and arrhythmia ECG datasets to recognize important patterns in sequential data. The ECG data is obtained from PhysioNet, which provides thousands of labeled recordings for training and testing. Additional clinical data from hospitals/clinics can be included for further validation with patient consent according to ethical protocols. The expected result is that this system can detect arrhythmias with high accuracy and optimal sensitivity. The benefits are to improve the quality of healthcare services and reduce the risk of serious complications.
Co-Authors Gabriela Septiani Simbolon Gresia Cesilia Sirait Jepri Banjarnahor Rivaldo Robertus Turnip Sarah Theresia Aruan