Multimodal Deep Learning and Feature Fusion for Automated Sleep Apnea Event Detection Using ECG and Respiratory Signals

Accurate and automated assessment of cardiorespiratory function is essential for physiological monitoring and the early detection of sleep-related disorders. Sleep apnea is a common but frequently underdiagnosed condition, affecting approximately 10% of middle-aged individuals, while polysomnography (PSG), the current diagnostic gold standard, is resource-intensive and requires substantial manual scoring. Advances in biosignal acquisition using modalities such as electrocardiography (ECG), photoplethysmography (PPG), electromyography (EMG), vibromyography (VMG), mechanomyography (MMG), and respiratory signals have enabled high-resolution and multimodal physiological analysis. In this context, deep learning techniques, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and hybrid CNN–LSTM architectures, offer automated feature extraction and the ability to capture complex spatial and temporal patterns in physiological signals. This study investigates a multimodal deep-learning framework for automated sleep-apnea event detection by combining learned representations from raw ECG and respiratory signals with engineered cardiorespiratory features, including heart-rate-variability (HRV) and respiration-related descriptors. Using the MIT-BIH Polysomnographic Database, 10,274 non-overlapping 30-second windows from 18 records were analyzed. To prevent record-level data leakage, the records were divided into locked training, validation, and test sets. A logistic-regression model based on engineered features, a one-dimensional CNN based on raw physiological signals, and a CNN-based feature-fusion model were evaluated. The multimodal fusion model achieved a test ROC-AUC of 0.7341, PR-AUC of 0.5778, accuracy of 0.7317, precision of 0.5689, recall of 0.7204, specificity of 0.7372, and F1-score of 0.6358. Compared with logistic regression, the fusion approach improved ROC-AUC by 0.0222 and F1-score by 0.0665, while outperforming the raw-signal CNN by 0.0833 and 0.1440, respectively. These findings indicate that combining learned raw-signal representations with compact engineered cardiorespiratory descriptors can improve sleep-apnea event classification compared with either strategy alone. Future extensions could incorporate hybrid CNN–LSTM architectures for improved temporal modeling, Transformer-based approaches for multimodal signal fusion, and diffusion-based generative models for realistic biosignal augmentation and robustness evaluation. Nevertheless, substantial variability across held-out records highlights the need for external multi-center validation and patient-level statistical evaluation before clinical deployment.