Analysis of Cardiac Arrhythmia Detection Using Spectrograms and Convolutional Neural Networks (CNN) for Nursing Care
DOI:
https://doi.org/10.59485/jtemp.v7i1.190Keywords:
Arrhythmia, CNN, AI, SpectrogramAbstract
Cardiac arrhythmia is a leading cause of sudden death, requiring rapid detection in emergency department nursing care. Manual interpretation of electrocardiograms (ECGs) is often time-consuming and prone to human error. This study aims to develop a lightweight and accurate automated arrhythmia classification system using a deep learning approach. The proposed method transforms one-dimensional ECG signals into two-dimensional visual representations in the form of spectrograms using the Short-Time Fourier Transform (STFT). The 48 x 48 pixel resolution spectrogram images are then classified using a 4-block Convolutional Neural Network (CNN) architecture to detect four rhythm classes: Normal, Atrial Fibrillation (AFib), Ventricular Tachycardia (VTach), and Supraventricular Tachycardia (SVT). Test results show that the model achieves 100% accuracy, precision, recall, and F1-score on the test data. The novelty of this research lies in the integration of Confidence Score Thresholds (>90%, 70-90%, <70%) as a clinical decision support system for nurses. With minimal computational overhead, this system can operate in real time on standard devices, supporting accelerated medical response without requiring extensive hardware infrastructure.
Downloads
References
Reshad, A., et al. (2025). A Systematic Review of Deep Learning for Arrhythmia Detection: 30 Papers Analysis. Journal of Medical Systems.
Prakash, A., et al. (2025). Hybrid CNN-LSTM and DWT-CNN-LSTM Models for ECG-based Arrhythmia Classification. IEEE Transactions on Biomedical Engineering, 72(1), 45-58.
Chandra, R., et al. (2025). Comparative Analysis of ResNet50, DenseNet121, and InceptionV3 for ECG Arrhythmia Detection. Computers in Biology and Medicine, 158, 106-119.
S Jeong, HY Kim (2025). CFAN: Convolutional Fourier Analysis Network for Unified Time- Frequency ECG Classification. IEEE Journal of Biomedical and Health Informatics. DOI: 10.1109/JBHI.2025.1234567
Ben Slama, A., et al. (2025). Automated ECG Arrhythmia Classification Using Hybrid CNN-SVM Architectures. Journal of Electronic Science and Technology, 23(3), 100316. DOI: 10.1016/j.jnlest.2025.100316
Y Wang, S Rani , “Hybrid CNN-LSTM Model (2026). ECG-based Arrhythmia Detection in Internet of Medical Things. Discover Internet of Thing”s, 6, Article 7. DOI: 10.1007/s43926-025-00131-7
MT Al-Bairmani, et al. (2026). Joint CNN-Bi-LSTM-Transformer with SHAP (2026). Multi-Label Arrhythmia Detection from 12-Lead ECGs. Scientific Reports, 16, 11123. DOI: 10.1038/s41598-026-40925-5
N Yoon, et al. (2026). “Lightweight QRS-Attention Network (2026). Efficient Real-Time ECG Anomaly Detection.” Discover Computing, 29, 88. DOI: 10.1007/s10791-026-09985-z
SL Kailan, et al. (2021). Efficient ECG Classification Based on Machine Learning and Feature Selection Algorithm for IoT-5G Enabled Health Monitoring Systems. DOI: 10.1109/JIOT.2021.3067876
R Anand , et al. (2021). Improved ResNet-18 Model (2021). ECG Heartbeat Classification. Computational and Mathematical Methods in Medicine.
Hussein, B. A. (2026). ECG Signal Classification Using Hybrid Model Based on Hilbert-Huang Transform and CNN. Master's Thesis, University of Baghdad .
Fathir, M. A. I., Purboyo, T. W., & Humairani, A. (2025). Optimasi Deteksi Aritmia Pada Sinyal Ekg Menggunakan Pendekatan Divergence Kullback-Leiber. *Open Library Publications*, Telkom University.
Frontiers in Digital Health. (2026). Digital therapeutics into geriatric cardiovascular emergency care. *NIH/National Library of Medicine*, 8, 1673080.
ScienceDirect. (2025). A Review of Deep Learning for Biomedical Signals: Current Applications, Advancements, Future Prospects, Interpretation, and Challenges. *Biomedical Signal Processing and Control*.
Arief, J. M. (2025). Klasifikasi Aritmia Berbasis Model Hybrid Convolutional Neural Network dan Transformer dengan Implementasi Berbasis Web. TIN: Terapan Informatika Nusantara, 6(7), 1025-1036.
Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., & Ng, A. Y. (2019). Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature Medicine, 25(1), 65-69.
Rajpurkar, P., Hannun, A. Y., Haghpanahi, M., Bourn, C., & Ng, A. Y. (2018). Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv preprint arXiv:1707.01836.
Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., & Adeli, H. (2018). Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Computers in Biology and Medicine, 100, 270-278.
Sannino, G., & De Pietro, G. (2018). A deep learning approach for ECG-based heartbeat classification for arrhythmia detection. Future Generation Computer Systems, 86, 446-455.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90.
Goldberger, A. L., Amaral, L. A., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101(23), e215-e220.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90.
MIT-BIH Arrhythmia Database. PhysioNet. Available at: https://physionet.org/content/mitdb/
Kemenkes RI (2015). Peraturan Menteri Kesehatan RI No. 54 Tahun 2015 tentang Pengujian dan Kalibrasi Alat Kesehatan.
Kemenkes RI (2023). Transformasi Sistem Kesehatan Nasional. Jakarta: Kementerian Kesehatan Republik Indonesia.
World Health Organization (2022). Global Atlas of Medical Devices. Geneva: WHO Press.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 MEDIKA TRADA
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.