目的 可穿戴设备能够长时间实时监测人体心脏状况，其在心电信号监测领域应用广泛。但目前仍没有公开的来自可穿戴设备的心电数据集，大部分心电信号分析算法都是针对医院设备所采集的心电数据。因此，本文使用IREALCARE 2.0柔性远程心电贴作为心电信号监测和采集设备制作了可穿戴设备的心电数据集。针对可穿戴心电数据干扰多、数据量大等特点，本文提出了一种针对可穿戴设备获得的心电信号进行自动分类的深层卷积神经网络，称之为时空卷积神经网络（time-spatial convolutional neural networks，TSCNN）。方法 将原始的长时间心电信号分割为单个的心搏并与滤波后不同频段的心搏数据组合成十通道的数据输入到TSCNN中。TSCNN对每个心搏使用时间卷积和空间滤波来提取丰富的特征。采用小卷积核级联卷积的方式提高分类性能，并降低网络的参数量和计算量。结果 在本文制作的心电数据集上进行了测试，并与其他4种心电分类算法：CNN（convolutional neural networks）、RNN（recurrent neural networks）、1-DCNN（1-dimensional convolution neural networks）和DCN（dense convolutional networks）进行了比较。实验结果显示，本文方法的分类准确率达到91.16%，优于其他4种方法。结论 本文方法面向可穿戴心电数据，获得了较好的分类性能，可以有效监控穿戴者是否出现了心电异常情况。

Objective Wearable devices are expanding in terms of electrocardiograms (ECG) because they can portably monitor the heart condition of a human for a long time. The key for wearable devices to monitor heart conditions in real time is to be able to process the collected ECG data automatically. Therefore, an efficient and reliable classification algorithm for ECG data from wearable devices should be designed. Many ECG classification algorithms have been proposed in recent years. They are mainly divided into two categories: one is based on handcrafted features, and the other is based on deep learning. Classification methods based on artificial features need to design various features manually, and many useful features are often ignored. The deep learning method can automatically extract features, and it has achieved good performance in image classification, object recognition, and natural language processing. However, no ECG data set from wearable devices is publicly available. Most ECG analysis algorithms are aimed at ECG data collected using hospital equipment. The ECG signal collected using wearable devices is susceptible to various interferences due to the body movement of people and changes in the surrounding environment, resulting in various noises in the signal. Therefore, many methods that perform well in ECG data sets collected using hospital equipment cannot achieve accurate classification of ECG data from wearable devices. In this study, we used IREALCARE 2.0 flexible cardiac monitor patch as the wearable device to collect ECG signals and make ECG data sets. It has the characteristics of small size, lightweight, and accurate and reliable measurement. The ECG data set came from 38 subjects and mainly included five types of heartbeats: normal, ventricular premature beat (VPB), supraventricular premature beat (SPB), atrial fibrillation (AF), and interference. In accordance with the characteristics of ECG data from wearable devices, such as considerable interference and large amount of data, this study proposed a deep convolutional neural network (CNN) named time-spatial CNN (TSCNN) for the automatic classification and analysis of ECG signals from wearable devices. Method TSCNN consisted of seven convolutional layers, four pooling layers, and one fully connected layer. It also comprised a convolution-pooling block, three convolution-convolution-pooling blocks, and a classification layer. First, the original long-term ECG signals were divided into separate heartbeats. We regarded the 600 data points around R-wave coordinates as one heartbeat, combined them with filtered heartbeat data of different frequency bands into 10 channels of data, and input them into TSCNN. Second, we applied convolution over time and spatial filtering for each heartbeat, which could effectively expand the receptive field of the network to extract abundant features. Lastly, cascaded convolution with a small convolution kernel was applied to improve classification performance and reduce the amount of network parameters and computation. In accordance with the input data size of each layer, we adopted three convolution kernels of different sizes. The convolution kernels in the three convolution-convolution-pooling blocks were 10×1, 5×1, and 3×1 in sequence with stride 1×1. The fully connected layer was used as the classification layer. Some regularized methods, such as dropout and batch normalization, were adopted to avoid overfitting. The batch normalization layer was placed after the convolutional layer and before the nonlinear layer, which made the input value of the nonlinear transformation function fall into a region sensitive to the input, to avoid the problem of gradient disappearance. Dropout randomly set some hidden layer nodes to zero during each iteration, which could reduce the interaction among hidden layer nodes. Result The proposed method was evaluated on the ECG data set produced in this paper and compared with four other ECG classification algorithms: CNN, recurrent neural network, 1D CNN (1-DCNN), and dense convolutional network (DCN). We used 10 records containing 138 853 heartbeats as the training set, and the test data were collected from 28 subjects, which contained 241 896 heartbeats. Results showed that the method in this paper achieved an overall accuracy of 91.16%, and the accuracies of normal, VPB, SPB, AF, and interference were 89.17%, 92.99%, 62.03%, 91.56%, and 94.89%, respectively. Comparative experiments indicated that our method was better than the four other methods in overall accuracy and the accuracies of normal, VPB, SPB, and AF and achieved a great improvement. Compared with 1-DCNN, the proposed method demonstrated increased overall accuracy by 7.25% and increased accuracies of normal, VPB, SPB, and AF by 18.13%, 12.67%, 9.29%, and 23.96%, respectively. Compared with DCN, the proposed method exhibited increased overall accuracy by 5.88% and increased accuracies of normal, VPB, SPB, and AF by 16.88%, 8.41%, 11.73%, and 20.32%, respectively. In addition, the classification results of our method were balanced, and the recognition capability for each class was similar. We also conducted a comparative experiment with and without spatial filtering. The experimental results demonstrated that spatial filtering could improve the classification performance of the network, and the overall accuracy increased from 88.17% to 91.16%. We further trained and evaluated deep networks that use cascaded convolution with a small convolution kernel and shallow networks that do not use it. The comparative experiment results showed that using cascaded convolution with a small convolution kernel could improve the classification capability of the network, in which the overall accuracy was increased by 1.56%; it also effectively reduced the amount of network parameters and calculations, thus increasing the processing speed. Conclusion In this study, we proposed TSCNN to classify ECG signals from wearable devices. The experimental results indicated that the proposed method can achieve good classification performance for ECG data from wearable devices and effectively monitor whether the wearer has abnormal ECG.