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

• Author(s): Paramita Ray; Swarna Ganga Priya Ponnada
• Paper ID: 1719782
• Page: 1550-1559
• Published Date: 17-07-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 10 Issue 1 July-2026

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

Citations

IRE Journals:
Paramita Ray, Swarna Ganga Priya Ponnada "A Self-Attention-Based Deep Learning Framework for Early Prediction of Cardiac Disease Using Sleep Apnea Signals" Iconic Research And Engineering Journals Volume 10 Issue 1 2026 Page 1550-1559

IEEE:
Paramita Ray, Swarna Ganga Priya Ponnada "A Self-Attention-Based Deep Learning Framework for Early Prediction of Cardiac Disease Using Sleep Apnea Signals" Iconic Research And Engineering Journals, vol. 10, no. 1, Jul. 2026

APA:
Paramita Ray, Swarna Ganga Priya Ponnada (2026). A Self-Attention-Based Deep Learning Framework for Early Prediction of Cardiac Disease Using Sleep Apnea Signals. Iconic Research And Engineering Journals, 10(1).

MLA:
Paramita Ray, Swarna Ganga Priya Ponnada "A Self-Attention-Based Deep Learning Framework for Early Prediction of Cardiac Disease Using Sleep Apnea Signals" Iconic Research And Engineering Journals, vol. 10, no. 1, Jul. 2026.

BibTeX

@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}
}