<![CDATA[Sleep apnea is a serious and common breathing disorder that occurs during sleep, characterized by repeated pauses in breathing that can increase the risk of hypertension, heart disease, and stroke. Early detection of sleep apnea is crucial, but conventional methods, such as polysomnography, are expensive, complex, and inefficient for mass screening. Therefore, an automated system based on physiological signals such as an electrocardiogram (ECG) is needed for a more practical and efficient approach. This study proposes a sleep apnea classification model utilizing a combination of 1D Convolutional Sparse Autoencoder (1DCSAE), Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU) architectures, referred to as the SAE-DEEP model. This method is designed to automatically extract features while minimizing the need for preprocessing. Four testing scenarios were conducted to evaluate the impact of signal reconstruction and preprocessing on classification performance. Experimental results show that the CNN-GRU model with signal reconstruction using 1DCSAE achieves an accuracy of 89.8%, a sensitivity of 90.1%, and a specificity of 89.2%, demonstrating balanced and stable classification performance. Additionally, this model was proven to work effectively without complex preprocessing steps, making it a potential solution for efficient sleep apnea detection systems. These findings could contribute to the development of more straightforward, reliable, and clinically viable ECG-based classification systems, as well as wearable devices. In doing so, the proposed model addresses a critical gap in sleep apnea screening, underscoring the urgent need for accessible and cost-effective diagnostic tools. ]]>
