Wearable devices are increasingly popular for health monitoring via electrocardiograms (ECGs) as they can portably monitor heart conditions over a long time. However, so far there are no publicly available ECG data sets collected from wearable devices. Most ECG analysis algorithms target ECG data collected by hospital equipment. In the present study, we used the IREALCARE2.0 Flexible Cardiac Monitor Patch as the wearable device to collect ECG signals and formed ECG data sets. Wearable ECG data tended to contain more interference and be large in size. This article proposed a deep CNN approach, named time–spatial convolutional neural networks (TSCNNs), for the automatic classification and analysis of ECG signals from wearable devices. First, the original long-term ECG signals were divided into separate heartbeats and input into the TSCNN. Second, we applied convolution over time and spatial filtering for each heartbeat to extract abundant features. Finally, the cascaded small-scale kernel convolution was applied to improve classification performance and reduce the number of network parameters. To avoid overfitting, some regularized methods such as dropout and batch normalization were adopted. In the experiments, the method proposed in this letter is compared with other eight ECG classification algorithms. Our method attained the highest classification accuracy. The experimental results indicated that the proposed method can achieve better performance for wearable ECG data and can effectively monitor whether the wearer has an abnormal ECG.

中文翻译：

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基于卷积神经网络的长期穿戴式心电信号监测与分析


可穿戴设备通过心电图 (ECG) 进行健康监测越来越受欢迎，因为它们可以长时间便携式监测心脏状况。然而，到目前为止，还没有从可穿戴设备收集的公开可用的心电图数据集。大多数心电图分析算法都针对医院设备收集的心电图数据。在本研究中，我们使用IREALCARE2.0柔性心脏监护贴片作为可穿戴设备来收集心电信号并形成心电数据集。可穿戴心电图数据往往包含更多干扰且数据量较大。本文提出了一种深度 CNN 方法，称为时空卷积神经网络 (TSCNN)，用于对可穿戴设备的 ECG 信号进行自动分类和分析。首先，将原始的长期心电信号分为单独的心跳并输入到TSCNN中。其次，我们对每个心跳应用时间卷积和空间滤波来提取丰富的特征。最后，应用级联小规模核卷积来提高分类性能并减少网络参数数量。为了避免过度拟合，采用了一些正则化方法，例如 dropout 和批量归一化。在实验中，本文提出的方法与其他八种心电图分类算法进行了比较。我们的方法获得了最高的分类精度。实验结果表明，该方法对于可穿戴式心电图数据能够取得较好的性能，能够有效监测佩戴者心电图是否存在异常。