Proceedings of the MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025)

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Arrhythmia Detection in Patient ECGs Using Deep Convolutional Neural Networks with IoT-Enabled

Authors
Neha A. Bagar1, Pritika N. Patil2, *, Shradha A. Kumavat1, Aniket L. Sonawane1
1Alamuri Ratnamala Institute of Engineering and Technology, Sapgoan, India
2Anjuman-I-Islam’s Kalsekar Technical Campus, Panvel, India
*Corresponding author. Email: pritika.patil@aiktc.ac.in
Corresponding Author
Pritika N. Patil
Available Online 7 October 2025.
DOI
10.2991/978-94-6463-852-3_30How to use a DOI?
Keywords
Arrhythmia; DCNN (Deep Convolutional Neural Networks); Sinus Rhythm; SVM (Support Vector Machine); ECG (Electrocardiogram)
Abstract

Cardiac arrhythmia, a type of heart condition, is responsible for 12% of global deaths. While IoT-based health monitoring has advanced, the manual methods used have several limitations. Therefore, there’s a need for an automatic healthcare approach, specifically for identifying arrhythmia. We propose using an optimized deep convolutional neural network for this purpose. In our plan, we’ll use an IoT network to collect ECG signals from patients. These signals will be analyzed to classify arrhythmia, ensuring continuous patient health monitoring. Our proposed model, Accuracy sensitivity and specificity of an existing method to access its effectiveness using a performance matrix is compared with deep optimize convolutional neural network.

Copyright
© 2025 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 MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025)
Series
Advances in Intelligent Systems Research
Publication Date
7 October 2025
ISBN
978-94-6463-852-3
ISSN
1951-6851
DOI
10.2991/978-94-6463-852-3_30How to use a DOI?
Copyright
© 2025 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  - Neha A. Bagar
AU  - Pritika N. Patil
AU  - Shradha A. Kumavat
AU  - Aniket L. Sonawane
PY  - 2025
DA  - 2025/10/07
TI  - Arrhythmia Detection in Patient ECGs Using Deep Convolutional Neural Networks with IoT-Enabled
BT  - Proceedings of the MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025)
PB  - Atlantis Press
SP  - 471
EP  - 480
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-852-3_30
DO  - 10.2991/978-94-6463-852-3_30
ID  - Bagar2025
ER  -