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Obstructive sleep apnea (OSA) is a sleep breathing disorder that can seriously affect the health of patients. The manual diagnostic of OSA through the Polysomnography (PSG) recordings is time-consuming and tedious. Electrocardiogram (ECG) signals have been an alternative for OSA detection. This paper proposes a CNN-Transformer architecture for automatic OSA detection based on single-channel ECG signals. The proposed architecture has two fundamental parts. The first part has the aim of learning a feature representation from ECG signals by using the CNN. The second part consists mainly of the Transformer, a model structure built solely with self-attention mechanism, which is used to model the global temporal context and to perform classification tasks. The effectiveness of the proposed method was validated on Apnea-ECG dataset. The dataset consists of 70 ECG recordings with an annotation for each minute of each recording. The current and adjacent 1-min epochs were combined to form the 3-min input epoch. Besides, experiments were set up with different baseline deep learning models for sequence modeling to verify their effects on classification performance. The per-segment classification accuracy reached 88.2% and the area under the receiver operating characteristic curve (AUC) was 0.95. The per-recording classification accuracy reached 100% and the mean absolute error (MAE) was 4.33. Experimental results demonstrate that the Transformer structure and a 3-min input time window both effectively improve the classification performance. The proposed method can accurately detect OSA from single-channel ECG signals and provides a promising and reliable solution for home portable detection of OSA. • A novel deep learning architecture is proposed for OSA detection based on raw single-channel ECG signals. • The CNN and Transformer structures are implemented to enhance the model performance. • The current and adjacent 1-min epochs are combined to form the 3-min contextual input epoch. • Achieving the state-of-the-art performance on the Apnea-ECG dataset. [ABSTRACT FROM AUTHOR]