Progress in wearable techniques makes the long-term daily electrocar-diogram (ECG) monitoring possible. It allows the physician to diagnose heart diseases and risks more accurately. Premature ventricular contrac-tion (PVC) is one of the most common cardiac arrhythmias and its accu-rate detection is particularly important for real-time monitoring life-threatening arrhythmias. This paper proposed a method by combining the modified frequency slice wavelet transform (MFSWT) and convolutional neural network (CNN). Training data are from the 2018 China physiolog-ical signal challenge (934 PVC and 906 non-PVC recordings). The first 10-s ECG waveforms in each recording were transformed into 2-D time-frequency images using MFSWT, with a fixed pixel size of 300 100. Then, the 2-D images were fed into a CNN model for feature extraction and PVC/non-PVC classification. A 25-layer CNN structure was constructed. Except the input and out-put layers, it includes five convolution layers with kernel size of 3 3, five dropout layers, five ReLU layers, five maximum pooling layers with kernel size of 2 2, a flatten layer and two fully connected layers. Then, we employed a balanced image data set to train the model. Test data were recorded from 12-lead Smart ECG vests. ECGs were segmented into 10-s episodes and were further manually labeled as PVC or non-PVC by clini-cal experts, including 775 PVC recordings and 742 non-PVC recordings. Results on the test data showed that, the proposed combination method achieved a high accuracy of 97.89% for PVC/non-PVC episodes classifi-cation, indicating that the combination method of MFSWT and CNN pro-vides possible to accurately identify PVC from the wearable ECG record-ings.