Proceedings of the 1st International Conference of Technology, Innovation, Design & Enterprise (ICTIDE 2025)

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Arrhythmia Detection Based on ECG Signals Using The K-Nearest Neighbor Algorithm

Authors
Mardi Turnip1, *, Poltak Sihombing2, Romi Fadillah Rahmat2, Suherman Suherman3
1Faculty of Science and Technology, Universitas Prima Indonesia, Medan, Indonesia
2Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Medan, Indonesia
3Faculty of Engineering, Universitas Sumatera Utara, Medan, Indonesia
*Corresponding author. Email: marditurnip@unprimdn.ac.id
Corresponding Author
Mardi Turnip
Available Online 5 March 2026.
DOI
10.2991/978-94-6463-998-8_11How to use a DOI?
Keywords
Arrhythmia Detection; K-Nearest Neighbor; Heart Monitoring; ECG
Abstract

The increasing prevalence of arrhythmia is a growing concern in heart rhythm disorders, posing a significant risk if not detected in time. The application of the K-Nearest Neighbor (KNN) algorithm enhances the classification of heart rhythms, enabling early arrhythmia detection in a more personalized and precise manner. In this study, the K-Nearest Neighbor algorithm is employed to classify heart rhythms using electrocardiogram (ECG) data. This approach allows for feature variation, which can lead to improved results through tailored methodologies. The test data is obtained from ECG recordings of individuals performing various physical activities, captured under both normal and arrhythmic conditions. Experimental scenarios may also include data collected from different healthcare facilities. It is anticipated that arrhythmia detection accuracy will improve across varying levels of physical activity using the KNN model, which focuses on individual heart rhythm patterns. The primary goal is to detect arrhythmia both early and accurately.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 1st International Conference of Technology, Innovation, Design & Enterprise (ICTIDE 2025)
Series
Advances in Engineering Research
Publication Date
5 March 2026
ISBN
978-94-6463-998-8
ISSN
2352-5401
DOI
10.2991/978-94-6463-998-8_11How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Mardi Turnip
AU  - Poltak Sihombing
AU  - Romi Fadillah Rahmat
AU  - Suherman Suherman
PY  - 2026
DA  - 2026/03/05
TI  - Arrhythmia Detection Based on ECG Signals Using The K-Nearest Neighbor Algorithm
BT  - Proceedings of the 1st International Conference of Technology, Innovation, Design & Enterprise (ICTIDE 2025)
PB  - Atlantis Press
SP  - 71
EP  - 88
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-998-8_11
DO  - 10.2991/978-94-6463-998-8_11
ID  - Turnip2026
ER  -