IEEE J Biomed Health Inform. 2018 Sep;22(5):1434-1444. doi: 10.1109/JBHI.2017.2771768. Epub 2017 Nov 10.
In this paper, a novel algorithm based on a convolutional neural network (CNN) is proposed for myocardial infarction detection via multilead electrocardiogram (ECG). A beat segmentation algorithm utilizing multilead ECG is designed to obtain multilead beats, and fuzzy information granulation is adopted for preprocessing. Then, the beats are input into our multilead-CNN (ML-CNN), a novel model that includes sub two-dimensional (2-D) convolutional layers and lead asymmetric pooling (LAP) layers. As different leads represent various angles of the same heart, LAP can capture multiscale features of different leads, exploiting the individual characteristics of each lead. In addition, sub 2-D convolution can utilize the holistic characters of all the leads. It uses 1-D kernels shared among the different leads to generate local optimal features. These strategies make the ML-CNN suitable for multilead ECG processing. To evaluate our algorithm, actual ECG datasets from the PTB diagnostic database are used. The sensitivity of our algorithm is 95.40%, the specificity is 97.37%, and the accuracy is 96.00% in the experiments. Targeting lightweight mobile healthcare applications, real-time analyses are performed on both MATLAB and ARM Cortex-A9 platforms. The average processing times for each heartbeat are approximately 17.10 and 26.75 ms, respectively, which indicate that this method has good potential for mobile healthcare applications.
本文提出了一种基于卷积神经网络（CNN）的新算法，用于通过多导联心电图（ECG）检测心肌梗死。设计了一种利用多导联 ECG 的心动周期分段算法来获取多导联心动周期，并采用模糊信息粒度进行预处理。然后，将这些心动周期输入到我们的多导联-CNN（ML-CNN）中，这是一种新的模型，包括子二维（2-D）卷积层和导联不对称池化（LAP）层。由于不同导联代表同一心脏的不同角度，LAP 可以捕获不同导联的多尺度特征，利用每个导联的个体特征。此外，子 2-D 卷积可以利用所有导联的整体特征。它使用在不同导联之间共享的 1-D 核来生成局部最优特征。这些策略使 ML-CNN 适用于多导联 ECG 处理。为了评估我们的算法，使用了来自 PTB 诊断数据库的实际 ECG 数据集。在实验中，我们的算法的灵敏度为 95.40%，特异性为 97.37%，准确性为 96.00%。针对轻量级移动医疗保健应用，在 MATLAB 和 ARM Cortex-A9 平台上分别进行了实时分析。每个心跳的平均处理时间分别约为 17.10 和 26.75 毫秒，这表明该方法在移动医疗保健应用中有很好的应用潜力。
