Deep Learning for ECG Anomaly Detection: A Robust Real-Time Solution for Cancer Prediction
DOI:
https://doi.org/10.32628/CSEIT25112546Keywords:
Deep Learning, ECG Anomaly Detection, Breast Cancer, Brain Tumors, Supervised Learning, Convolutional Neural Networks, LSTM, Transformers, Real-Time DiagnosisAbstract
Early and accurate detection of cancer-related anomalies in electrocardiograms (ECG) can significantly improve patient outcomes. This paper presents a deep learning-based ECG anomaly detection system designed for the real-time identification of breast cancer and brain tumors. By integrating convolutional neural networks (CNNs) with recurrent neural networks (RNNs), transformer-based attention, ensemble learning, and additional strategies to address data imbalance and domain adaptation, we classify subtle ECG patterns that could indicate latent malignancies. Publicly available datasets—including the MIT-BIH Arrhythmia Database and PhysioNet’s PTB-XL are used to train, evaluate, and validate the system’s real-time capabilities. Experimental results reveal superior performance compared to traditional analytical approaches and underscore the feasibility of AI-driven ECG-based oncology diagnostics. These findings pave the way for an automated, non-invasive screening tool that can be seamlessly integrated into clinical workflows, delivering timely and accurate alerts for at-risk individuals.
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Moody, G. B., & Mark, R. G. (2001). "The Impact of the MIT-BIH Arrhythmia Database." IEEE Engineering in Medicine and Biology. DOI: 10.1109/51.932724
Goldberger, A. L., et al. (2000). "PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals." Circulation. DOI: 10.1161/01.CIR.101.23.e215
Hannun, A. Y., et al. (2019). "Cardiologist-Level Arrhythmia Detection with Deep Neural Networks." Nature Medicine. DOI: 10.1038/s41591-018-0268-3
Esteva, A., et al. (2017). "Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks." Nature. DOI: 10.1038/nature21056
PhysioNet. "MIT-BIH Arrhythmia Database." DOI: 10.13026/C2F305
Wagner, P., et al. (2020). "PTB-XL, a Large Publicly Available Electrocardiography Dataset." Scientific Data. DOI: 10.1038/s41597-020-0495-6
Street, W. N., et al. (1993). "Breast Cancer Wisconsin (Diagnostic) Dataset." University of Wisconsin-Madison. DOI: 10.34740/Wisconsin/bc-dataset
Kaggle. "Brain Tumor MRI Dataset." Kaggle Datasets. DOI: 10.34740/Kaggle/ds/BrainTumor
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). "SMOTE: Synthetic Minority Over-sampling Technique." Journal of Artificial Intelligence Research 16: 321–357.
Privacy-Preserving Federated Learning in Healthcare - A Secure AI Framework. (2024). International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(3), 703-707. Available at DOI: https://doi.org/10.32628/CSEIT2410347
Bhavani Sankar Telaprolu, "Advancing AI-Powered Wearables - A Novel Approach for Real-Time Health Monitoring" International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 9, Issue 3, pp.722-727, May-June-2023. Available at DOI: https://doi.org/10.32628/CSEIT2390383
Sankar, T. B., Kumar, S. A., & Tarun, G. B. S. (2018). A Sustainable and Automated Irrigation and Field Monitoring System Using IoT. International Journal of Technical Innovation in Modern Engineering & Science, 4(12), 268–274.https://www.ijtimes.com/index.php/ijtimes/article/view/186, DOI: https://doi.org/10.5281/zenodo.14948446
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). "Focal Loss for Dense Object Detection." IEEE International Conference on Computer Vision (ICCV). DOI: 10.1109/ICCV.2017.324
Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems (NeurIPS), 30. arXiv:1706.03762. https://arxiv.org/abs/1706.03762
THE ROLE OF DATA ENGINEERS AND ANALYSTS IN HEALTH INSURANCE AND COORDINATION. (2025). International Journal of Data Science and Machine Learning, 5(01), 11-14. https://doi.org/10.55640/ijdsml-05-01-03
McMahan, B., et al. (2017). Communication-efficient federated learning. Advances in Neural Information Processing Systems (NeurIPS), 30. arXiv:1710.06963. https://arxiv.org/abs/1710.06963
Abadi, M., et al. (2016). TensorFlow: A system for large-scale machine learning. USENIX Symposium on Operating Systems Design and Implementation (OSDI), 265-283.
Schroff, F., et al. (2015). FaceNet: A unified embedding for face recognition and clustering. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 815-823.
Esteva, A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.
Deng, J., et al. (2009). ImageNet: A large-scale hierarchical image database. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 248-255.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
Goldberger, A. L., et al. (2000). PhysioNet: Components of a complex physiologic signals archive. Circulation, 101(21), e215-e220. https://doi.org/10.1161/01.CIR.101.23.e215
Dataset: Goldberger, A. L., et al. (2000). MIT-BIH Arrhythmia Database. PhysioNet. https://doi.org/10.13026/C2F305
Yildirim, O., et al. (2018). ECG classification using deep learning models. Biomedical Signal Processing and Control, 43, 152-162. https://doi.org/10.1016/j.bspc.2018.03.005
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint, arXiv:1412.6980. https://arxiv.org/abs/1412.6980
Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training. International Conference on Machine Learning (ICML), 448-456.
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