Obstructive sleep apnoea (OSA) is a prevalent condition that can lead to various cardiovascular and cerebrovascular diseases, such as coronary heart disease, hypertension, and stroke, posing significant health risks. Polysomnography (PSG) is widely regarded as the most reliable method for detecting sleep apnoea (SA), but it is limited by a complex acquisition process and high costs. To address these issues, some studies have explored the use of single-lead signals, although they often result in lower accuracy due to noise-related information loss. Time context information has been applied to mitigate this issue, but it can lead to overfitting and category confusion. This paper introduces a novel approach utilising time sequence contrastive learning with single-lead electrocardiogram (ECG) signals to detect SA events and assess OSA severity. The proposed method features a Transformer encoder fusion module and a contrastive classification module. First, a multi-branch architecture is employed to extract features from different time scales of the ECG signal, which aids in SA detection. To further enhance the network's focus on the most relevant extracted features, a channel attention mechanism is incorporated to fuse features from different branches. Finally, contrastive learning is utilised to constrain the features, resulting in improved detection performance. A series of experiments were conducted on a public dataset to validate the effectiveness of the proposed method. The method achieved an accuracy of 91.50%, a precision of 92.06%, a sensitivity of 94.37%, a specificity of 86.89%, and an F1 score of 93.20%, outperforming state-of-the-art detection techniques.