The regular monitoring and accurate diagnosis of arrhythmia are critically important, leading to a reduction in mortality rate due to cardiovascular diseases (CVD) such as heart stroke or cardiac arrest. This paper proposes a novel convolutional neural network (CNN) model for arrhythmia classification. The proposed model offers the following improvements compared with traditional CNN models. Firstly, the multi-channel model can concatenate spectral and spatial feature maps. Secondly, the structural unit is composed of a depthwise separable convolution layer followed by activation and batch normalization layers. The structural unit offers effective utilization of network parameters. Also, the optimization of hyperparameters is done using Hyperopt library, based on Sequential Model-Based Global Optimization algorithm (SMBO). These improvements make the network more efficient and accurate for arrhythmia classification. The proposed model is evaluated using tenfold cross-validation following both subject-oriented inter-patient and class-oriented intra-patient evaluation protocols. Our model achieved 99.48% and 99.46% accuracy in VEB (ventricular ectopic beat) and SVEB (supraventricular ectopic beat) class classification, respectively. The model is compared with state-of-the-art models and has shown significant performance improvement.

心律失常的定期监测和准确诊断至关重要，可以降低心中风或心脏骤停等心血管疾病 (CVD) 造成的死亡率。本文提出了一种用于心律失常分类的新型卷积神经网络（CNN）模型。与传统的 CNN 模型相比，所提出的模型具有以下改进。首先，多通道模型可以连接光谱和空间特征图。其次，结构单元由深度可分离卷积层、激活层和批量归一化层组成。该结构单元提供了网络参数的有效利用。此外，超参数的优化是使用 Hyperopt 库基于基于序列模型的全局优化算法（SMBO）完成的。这些改进使得网络对于心律失常分类更加高效和准确。根据面向主题的患者间和面向类别的患者内评估协议，使用十倍交叉验证对所提出的模型进行评估。我们的模型在 VEB（室性异位搏动）和 SVEB（室上性异位搏动）类别分类方面分别实现了 99.48% 和 99.46% 的准确率。该模型与最先进的模型相比，显示出显着的性能改进。