Accurate classification of electrocardiogram (ECG) signals is of significant importance for automatic diagnosis of heart diseases. In order to enable intelligent classification of arrhythmias with high accuracy, an accurate classification method based intelligent ECG classifier using the fast compression residual convolutional neural networks (FCResNet) is proposed. In the proposed method, the maximal overlap wavelet packet transform (MOWPT), which provides a comprehensive time-scale paving pattern and possesses the time-invariance property, was utilized for decomposing the original ECG signals into sub-signal samples of different scales. Subsequently, the samples of the five arrhythmia types were utilized as input to the FCResNet such that the ECG arrhythmia types were identified and classified. In the proposed FCResNet model, a fast down-sampling module and several residual block structural units were incorporated. The proposed deep learning classifier can substantially alleviate the problems of low computational efficiency, difficult convergence and model degradation. Parameter optimizations of the FCResNet were investigated via single-factor experiments. The datasets from MIT-BIH arrhythmia database were employed to test the performance of the proposed deep learning classifier. An averaged accuracy of 98.79% was achieved when the number of the wide-stride convolution in fast down-sampling module was set as 2, the batch size parameter was set as 20 and wavelet subspaces of low frequency bands in MOWPT were selected as input of the classifier. These analysis results were compared with those generated by some comparison methods to validate the superiorities and enhancements of the proposed method.

心电图（ECG）信号的准确分类对于心脏病的自动诊断非常重要。为了实现对心律失常的智能分类，提出一种基于快速压缩残差卷积神经网络（FCResNet）的基于智能心电分类器的精确分类方法。在该方法中，利用最大重叠小波包变换（MOWPT）提供了一个完整的时标铺装图案，并具有时不变性，将原始的ECG信号分解为不同比例的子信号样本。随后，将五种心律失常类型的样本用作FCResNet的输入，以便识别和分类ECG心律失常类型。在建议的FCResNet模型中，快速降采样模块和几个剩余的块结构单元被合并。提出的深度学习分类器可以大大缓解计算效率低，收敛困难和模型退化的问题。通过单因素实验研究了FCResNet的参数优化。MIT-BIH心律失常数据库中的数据集用于测试所提出的深度学习分类器的性能。将快速下采样模块中的宽步长卷积数设置为2，将批大小参数设置为20，并选择MOWPT中低频带的小波子空间作为输入的平均准确度达到98.79％。分类器。