A Self-Attention-Based Deep Learning Framework for Early Prediction of Cardiac Disease Using Sleep Apnea Signals

References

[1] S. S. Mostafa, F. Mendonça, A. G. Ravelo-García, and F. Morgado-Dias, “A systematic review of detecting sleep apnea using deep learning,” Sensors, vol. 19, no. 22, Art. no. 4934, 2019. doi: 10.3390/s19224934.

[2] P. K. Tyagi and D. Agarwal, “Systematic review of automated sleep apnea detection based on physiological signal data using deep learning algorithm: A meta-analysis approach,” Biomedical Engineering Letters, vol. 13, no. 3, pp. 293–312, 2023. doi: 10.1007/s13534-023-00297-5.

[3] C. Sun, Y. Zhang, and H. Wang, “A systematic review of deep learning methods for modeling electrocardiograms during sleep,” Physiological Measurement, vol. 43, no. 8, 2022. doi: 10.1088/1361-6579/ac826e.

[4] K. Li, W. Pan, Y. Li, Q. Jiang, and G. Liu, “A method to detect sleep apnea based on deep neural network and hidden Markov model using single-lead ECG signal,” Neurocomputing, vol. 294, pp. 94–101, 2018.

[5] A. L. Goldberger et al., “PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals,” Circulation, vol. 101, no. 23, pp. e215–e220, 2000.

[6] T. Penzel, G. Moody, R. Mark, A. Goldberger, and J. Peter, “The Apnea-ECG Database,” in Proceedings of Computers in Cardiology, Cambridge, MA, USA, 2000, pp. 255–258.

[7] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105, 2012.

[8] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.

[9] A. Vaswani et al., “Attention Is All You Need,” in Advances in Neural Information Processing Systems, 2017, pp. 5998–6008.

[10] D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” in International Conference on Learning Representations (ICLR), 2015.

[11] H. Hannun et al., “Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network,” Nature Medicine, vol. 25, no. 1, pp. 65–69, 2019.

[12] P. Rajpurkar et al., “Cardiologist-level arrhythmia detection with convolutional neural networks,” arXiv:1707.01836, 2017.

[13] Z. Zhang, J. Pi, and B. Liu, “TROIKA: A general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise,” IEEE Transactions on Biomedical Engineering, vol. 62, no. 2, pp. 522–531, 2015.

[14] F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1251–1258.

[15] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778.

[16] J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of NAACL-HLT, 2019.

[17] J. Hu, L. Shen, and G. Sun, “Squeeze-and-Excitation Networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

[18] O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2015.

[19] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.

[20] PhysioNet, “PhysioNet: Research Resource for Complex Physiologic Signals,” Accessed: Jul. 2026.

@article{1719782,
      author = {Paramita Ray, Swarna Ganga Priya Ponnada},
      title = {A Self-Attention-Based Deep Learning Framework for Early Prediction of Cardiac Disease Using Sleep Apnea Signals},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {1},
      pages = {1550-1559},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1719782.pdf},
      abstract = {cardiovascular disease (CVD) is one of the leading causes of mortality worldwide, and early detection is essential for reducing its clinical and economic burden. Obstructive sleep apnea (OSA), a common sleep-related breathing disorder, has been identified as a significant risk factor for various cardiovascular conditions, including hypertension, arrhythmias, heart failure, and coronary artery disease. This project proposes a novel end-to-end deep learning framework that utilizes sleep apnea-related physiological signals for the early prediction of cardiac disease. The proposed model integrates one-dimensional Convolutional Neural Networks (1D-CNN), Bidirectional Long Short-Term Memory (BiLSTM) networks, and a Multi-Head Self-Attention mechanism to automatically extract spatial and temporal features from electrocardiogram (ECG), heart rate variability (HRV), blood oxygen saturation (SpO₂), and respiratory signals. The self-attention module enhances the model by identifying the most informative signal segments associated with cardiovascular abnormalities, thereby improving prediction accuracy and interpretability. The framework is evaluated using publicly available sleep apnea and cardiac datasets, with performance assessed through accuracy, precision, recall, F1-score, specificity, and ROC-AUC. The proposed system aims to provide an intelligent clinical decision-support tool for early cardiovascular risk assessment, enabling timely intervention, continuous patient monitoring, and improved healthcare outcomes through AI-driven predictive analytics.},
      keywords = {Cardiovascular DISEASE, Cnn, Electrocardiogram, Obstructive SLEEP APNEA},
      month = {July},
      doi = {https://doi.org/10.64388/IREV10I1-1719782}
  }