Deep convolutional neural networks (CNNs) have brought breakthroughs in processing clinical electrocardiograms (ECGs), speaker-independent speech and complex images. However, typical CNNs require a fixed input size while it is common to process variable-size data in practical use. Recurrent networks such as long short-term memory (LSTM) are capable of eliminating the restriction, but suffer from high computational complexity. In this paper, we propose local pattern aggregation-based deep-learning models to effectively deal with both problems. The novel network structure, called LPANet, has cropping and aggregation operations embedded into it. With these new features, LPANet can reduce the difficulty of tuning model parameters and thus tend to improve generalization performance. To demonstrate the effectiveness, we applied it to the problem of premature ventricular contraction detection and the experimental results shows that our proposed method has certain advantages compared to classical network models, such as CNN and LSTM.

翻译：深相神经网络(CNNs)在处理临床心电图(ECGs)、独立演讲和复杂图像方面带来了突破,然而,典型的CNN需要固定的输入量,而实际使用时处理可变大小数据则很常见。长期短期内存(LSTM)等经常网络能够消除限制,但具有很高的计算复杂性。在本文中,我们提出了基于本地模式的深层学习模型,以有效处理这两个问题。称为LPANet的新型网络结构有植入和集成操作。有了这些新功能,LPANet可以减少调制模型参数的困难,从而能够提高通用性。为了证明效果,我们应用了它来应对过早心电图收缩检测问题,实验结果表明,我们所提议的方法与CNN和LSTM等典型网络模型相比具有一定的优势。