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A Self-Attention-Based Deep Learning Framework for Early Prediction of Cardiac Disease Using Sleep Apnea Signals
Subject area: Science,Engineering and Technology · Area of research: Health Informatics
DOI: https://doi.org/10.64388/IREV10I1-1719782
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
How to cite this paper
@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}
}