Sleep apnea syndrome, as a common sleep-related respiratory disorder, has gained a lot of attention. Due to its complicated diagnosis process and high price, it has attracted many researchers to explore fast and convenient detection methods based on singlechannel signals. The research proposes a multi-scale convolutional neural network method for rapid detection of sleep apnea based on ECG signals. Compared with the traditional single-scale convolutional neural network, the method can effectively combine the detailed and abstract information of the signal, and improve the feature representation ability of the convolutional neural network. By verifying on the Apnea-ECG database provided by PhysioNet, the proposed multi-scale convolutional neural network obtains an accuracy of 85. 2%, sensitivity of 83. 1% and specificity of 86. 5%. Compared with existing methods, the method further improves the performance of sleep apnea detection.