Sleep apnea (SA) is a prevalent disorder that disrupts breathing during sleep, posing risks to multiple organs and potentially causing sudden death. The electrocardiogram (ECG) is vital for diagnosing SA due to its ability to identify irregular heart activity. This study introduces hybrid CNN models designed to automatically detect SA using a single-lead ECG signal. We validated our method through experiments with the Physionet Apnea-ECG dataset, which contains 70 single-lead ECG recordings annotated by medical professionals. Our results surpass the current state-of-the-art methods in accurately detecting SA from single-lead ECG signals, achieving an accuracy of 91.4% for per-segment classification and 100% for per-recording classification.