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

Q: What is the clinical evidence from this study?

Population: Cardiovascular Disease and Obstructive Sleep Apnea (n=4300). Intervention: CNN-BiLSTM-Multi-Head Self-Attention (MHSA) framework vs. Random Forest, CNN, and BiLSTM models. Primary outcome: Classification accuracy.

Q: What does this research mean for the field?

A deep learning framework integrating 1D-CNN, BiLSTM, and multi-head self-attention accurately predicts cardiac disease risk from sleep apnea-related physiological signals with an accuracy of 98.75% and an F1-score of 98.57%, outperforming traditional models. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Paramita Ray; Swarna Ganga Priya Ponnada

doi:10.64388/irev10i1-1719782

July 17, 2026Iconic Research and Engineering JournalsOpen Access

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

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Key result

Deep learning framework outperforms traditional models with ~99% accuracy predicting cardiac risk from sleep apnea signals.

n=4,300

Why the study?

Cardiovascular disease is a leading cause of global mortality requiring early detection, and obstructive sleep apnea is a major risk factor across diverse cardiovascular conditions.

Key Points

To develop an end-to-end deep learning framework that leverages sleep apnea physiological signals for early prediction of cardiovascular disease.
Designed a hybrid architecture combining one-dimensional Convolutional Neural Networks (1D-CNN) for local spatial features, Bidirectional Long Short-Term Memory (BiLSTM) for temporal patterns, and a Multi-Head Self-Attention mechanism.
Trained the model on multimodal time-series signals from publicly available physiological datasets, including electrocardiogram (ECG), heart rate variability (HRV), blood oxygen saturation (SpO₂), and respiration.
Assessed model performance using accuracy, precision, recall, F1-score, specificity, and receiver operating characteristic area under the curve (ROC-AUC).
The integrated deep learning model improved early cardiovascular disease detection relative to conventional machine learning and standalone deep learning approaches by automatically extracting spatial and temporal features without manual engineering.
The multi-head self-attention mechanism enhanced prediction performance and clinical interpretability by pinpointing the most informative signal segments tied to cardiovascular abnormalities.

Structured PICO

P

Population

A dataset of 4,300 physiological signal segments from publicly available databases used to evaluate a deep learning model for sleep apnea and cardiac risk classification.

I

Intervention

A hybrid deep learning framework integrating one-dimensional Convolutional Neural Networks (1D-CNN), Bidirectional Long Short-Term Memory (BiLSTM) networks, and a Multi-Head Self-Attention mechanism.

C

Comparator

Random Forest, CNN, and BiLSTM models.

O

Outcome

Classification performance metrics including accuracy, precision, recall, F1-score, specificity, Matthews Correlation Coefficient (MCC), and ROC-AUC.

Main Result

Absolute Event Rate: 98.75% vs 93.42%

A novel deep learning framework combining 1D-CNN, BiLSTM, and Multi-Head Self-Attention can accurately predict cardiac risk using sleep apnea-related physiological signals, outperforming traditional machine learning models.

Limitations

The study relies on retrospective public datasets rather than real-time clinical monitoring.
Future work is needed to incorporate multimodal wearable sensor data and explainable AI techniques to enhance clinical applicability.

Implication

May support early cardiac risk assessment from sleep apnea signals; leaves open prospective validation before clinical adoption.

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.

Discussion

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Cite This Study

Ray et al. (2026) studied Cardiovascular Disease and Obstructive Sleep Apnea (n=4,300). CNN-BiLSTM-Multi-Head Self-Attention (MHSA) framework vs. Random Forest, CNN, and BiLSTM models was evaluated on Classification accuracy. The proposed CNN-BiLSTM-MHSA deep learning framework achieved an accuracy of 98.75% and an F1-score of 98.57% in classifying sleep apnea-related physiological signals for cardiac risk prediction, outperforming traditional models.

synapsesocial.com/papers/6a5dba3f8bd453d3397ab703 https://doi.org/10.64388/irev10i1-1719782

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

Key Points

Structured PICO

Main Result

Limitations

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Structured PICO

Main Result

Limitations

Implication

Abstract

Discussion

Cite This Study

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