<![CDATA[Sleep apnea is an important disorder that involves frequent disruptions in breathing during sleep, which can result in numerous serious health issues, such as cognitive deterioration, cardiovascular illness, and heightened mortality risk. This study introduces a detailed model designed for the detection of sleep apnea using single-lead electrocardiogram signals, providing an accurate detection method. We can use single-lead ECG signals to get ECG-Derived Respiration (EDR). EDR combines important respiratory signals with RR intervals to help find sleep apnea more accurately. We structure the research process into seven systematic stages, ensuring a comprehensive approach to the issue. The process commences with the acquisition of data from the "Apnea-ECG Database" accessible on the PhysioNet platform, which underpins the ensuing analysis. Subsequent to data collection, we execute a sequence of preprocessing procedures, including segmentation, filtering, and R-peak detection, to enhance the ECG data for analysis. After that, we do feature extraction, which gives us 12 unique features from the RR interval and 6 features from the R-peak amplitude, which are both necessary for the model to work. The research subsequently utilizes feature engineering, implementing a Time Window methodology to encapsulate the temporal dynamics of the data. To ensure the results are robust, we conduct model evaluation using stratified K-fold cross-validation with five folds. The modeling technique employs a 1D Convolutional Neural Network (1D-CNN) utilizing the Adam optimizer. Ultimately, the performance assessment shows an accuracy score reaching 89.87%, sensitivity at 86.16%, specificity at 92.30%, and an AUC score of 0.96, attained with a Time Window size of 15. This model signifies a substantial improvement in performance relative to previous studies and serves as a feasible option for the detection of sleep apnea]]>
