Machine learning is poised to revolutionize medicine with algorithms that spot cardiac arrhythmia. An automated diagnostic approach can boost the efficacy of diagnosing life-threatening arrhythmia disorders in routine medical procedures. In this paper, we propose a deep learning network CLINet for ECG signal classification. Our network uses convolution, LSTM and involution layers to bring their unique advantages together. For both convolution and involution layers, we use multiple, large size kernels for multi-scale representation learning. CLINet does not require complicated pre-processing and can handle electrocardiograms of any length. Our network achieves 99.90% accuracy on the ICCAD dataset and 99.94% accuracy on the MIT-BIH dataset. With only 297 K parameters, our model can be easily embedded in smart wearable devices. The source code of CLINet is available at .

中文翻译：

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CLINet：一种用于心电信号分类的新型深度学习网络

机器学习有望通过发现心律失常的算法来彻底改变医学。自动诊断方法可以提高常规医疗程序中诊断危及生命的心律失常疾病的效率。在本文中，我们提出了一种用于心电信号分类的深度学习网络 CLINet。我们的网络使用卷积层、LSTM 层和对合层，将它们独特的优势结合在一起。对于卷积层和对合层，我们使用多个大尺寸内核进行多尺度表示学习。CLINet不需要复杂的预处理，可以处理任意长度的心电图。我们的网络在 ICCAD 数据集上实现了 99.90% 的准确率，在 MIT-BIH 数据集上实现了 99.94% 的准确率。只需 297 K 个参数，我们的模型就可以轻松嵌入到智能可穿戴设备中。CLINet 的源代码可在 处获得。