###### Abstract  
Cardiovascular disease and its consequences on human health have never stopped and even show a trend of appearing in increasingly younger generations. The establishment of an excellent deep learning algorithm model to assist physicians in identifying and the early screening of ECG abnormalities can effectively improve the accuracy of diagnosis. Therefore, in this study, the deep residual network model is adapted for feature extraction and classification of ECG signals by pooling embedded into layers and double channel connection. At the same time, the wavelet adaptive threshold denoising algorithm is used to complete the high signal-to-noise filtering of ECG signals. Then, the alternate pooling residual network (APRN) is compared with the convolutional neural network (CNN), CNN with one residual unit (CNN-R), and the deep residual network (ResNet-18) using ECG datasets from the American MIT-BIH arrhythmia and ST segment abnormality database, European ST-T database, and sudden cardiac death ambulatory ECG database. The results are as follows: The average classification accuracy of the APRN on the four datasets is 97.89%, while the accuracies on CNN, CNN-R, and ResNet-18 are 97.17%, 97.53%, and 97.73%, respectively. In addition, compared with ResNet-18, the classification accuracy of our APRN on each class of data improves by 16.44% in total. alternate pooling residual network; wavelet adaptive thresholding denoising; ECG database; depth residual network +
Footnote †: journal: Medical Image Analysis  
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the wavelet adaptive threshold denoising algorithm [7] has been widely studied in signal denoising [8] and shows good application effect. [PERSON] put forward a kind of usage for ECG signal wavelet domain denoising of the mother wavelet function selection method, which allows the obtained de-noised ECG signal based on the wavelet to preserve the necessary information [9]. [PERSON] suggested a means to use adaptive double-threshold filtering and the discrete wavelet transform as an effective denoising of ECG signals, and the algorithm was applied to several signals in the MIT-BIH arrhythmia database [10]. At the same time, the division of the ECG signal before classification needs dynamic adjustments according to the sampling rate and a fixed point. Integrated application of the appropriate filtering and the piecewise method can effectively improve the accuracy of ECG classification. Exploring efficient, fast, and accurate ECG recognition algorithms have been the focus of many scholars [11]. [PERSON] proposed a pre-trained generic convolutional neural network (GCNN) for personal ECG recognition and classification [12], where the heartbeat is first used for training, rather than differentiating between patients. Based on the GCNN, a Tuned Dedicated CNN is applied for the corresponding individual, reaching an accuracy rate of 94.70% on the test set. [PERSON] proposed multiple training with CNN and improved the accuracy of ECG recognition by adjusting the quantity of neurons in the hidden layer [13], reaching a final accuracy rate of 99.09% on the test set. [PERSON] proposed a multi-lead neural network structure based on three residual blocks, and, through training of single-lead data and feature fusion of 12 lead training results, carried out ECG signal recognition and classification [14]; their resulting test set had an accuracy of 95.49%. [PERSON] proposed a multi-channel and multi-scale deep neural network (DNN) model, an end-to-end structure that can classify ECG signals without any feature extraction. Finally, the accuracy rate on the test set was approximately 93.00% [15]. However, most ECG classification algorithms have only been tested on one type of data, and, thus, have unvalidated generalization.