Arrhythmia is a cardiovascular disease that manifests irregular heartbeats. In arrhythmia detection, the electrocardiogram (ECG) signal is an important diagnostic technique. However, manually evaluating ECG signals is a complicated and time-consuming task. With the application of convolutional neural networks (CNNs), the evaluation process has been accelerated and the performance is improved. It is noteworthy that the performance of CNNs heavily depends on their architecture design, which is a complex process grounded on expert experience and trial-and-error. In this paper, we propose a novel approach, Heart-Darts, to efficiently classify the ECG signals by automatically designing the CNN model with the differentiable architecture search (i.e., Darts, a cell-based neural architecture search method). Specifically, we initially search a cell architecture by Darts and then customize a novel CNN model for ECG classification based on the obtained cells. To investigate the efficiency of the proposed method, we evaluate the constructed model on the MIT-BIH arrhythmia database. Additionally, the extensibility of the proposed CNN model is validated on two other new databases. Extensive experimental results demonstrate that the proposed method outperforms several state-of-the-art CNN models in ECG classification in terms of both performance and generalization capability.
翻译:心电图信号是一个重要的诊断技术。然而,人工评价心电图信号是一项复杂而耗时的任务。随着神经神经神经网络的应用,评估过程已经加快,性能也得到了改进。值得注意的是,CNN的性能在很大程度上取决于其建筑设计,这是一个基于专家经验和试验与高度判断的复杂过程。在本文中,我们提出一种新的方法,即心电图信号是一个重要的诊断技术,通过自动设计CNN模型和不同的建筑搜索(即Darts,基于细胞的神经结构搜索方法)对ECG信号进行高效分类。具体地说,我们最初通过Darts搜索一个细胞结构结构,然后根据获得的细胞定制一个新的CNNECG分类模型。为了调查拟议方法的效率,我们评估了MIT-BIH 电荷数据库中构建的模式。此外,拟议的CNN模型的可视性能通过自动设计CNN模型,以不同的结构搜索(即Darts,一个基于细胞神经神经结构搜索方法的细胞结构搜索方法)。具体地说,我们先由Darts搜索一个细胞结构结构结构结构,然后根据获得的细胞进行定制一个新型CNN模型分类。为了调查拟议方法的效率,我们评估MIT-BIH ARH数据库的模型的模型的模型的模型的模型。此外,在两个新的模型的实验性能测试方法都展示了CMS-G-CAR-CM-C-C-C-C-CAS-C-C-C-CA-CA-C-C-C-C-CA-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-