卷积神经网络混合模型的心律失常分类算法

Arrhythmia classification algorithm based on convolutional neural network hybrid model

摘要:

心律失常表现为不规则的心跳,心律失常类型的判断是心血管疾病早期预防和诊断的关键.为提高心律失常分类的准确率和速度,实现心律失常类型的自动识别,研究并提出了一种以卷积神经网络 (convolutional neural network, CNN) 为核心的7层混合模型结构.为保持心拍的完整性,根据R-R间期对心电信号进行动态分割得到不同长度的心拍.通过卷积层卷积核的滑动提取心拍的局部特征,平均池化层进行下采样,降低特征的维度.空间金字塔池化 (spatial pyramid pooling, SPP)层以不同的池化步长二次提取心拍特征,不同长度的输入特征经过SPP层的特征融合后得到相同长度的输出特征.利用极限学习机 (extreme learning machine, ELM) 作为分类器可以提高分类的速度,缩短训练时间.使用MIT-BIH数据集和十折交叉验证方法验证心律失常4分类模型的有效性,最终得出在测试集上分类总体准确率为99.16%,灵敏度为99.85%,特异性为98.89%,精度为99.85%.在相同软件环境下验证混合模型与单个模型的准确率与训练时间,实验结果表明:混合模型能以更少的训练时间获得更高的准确率,为快速准确地识别心律失常类型提供了一种可行方案.

Abstract:

Arrhythmia is characterized by irregular heartbeats, and arrhythmia classification plays a key role in early prevention and diagnosis of cardiovascular diseases. In order to improve the accuracy and speed of arrhythmia classification and realize the automatic recognition of arrhythmia types, a seven-layer hybrid model based on convolutional neural network (CNN) was proposed. To maintain the integrity of the beats, the electrocardiogram signal was dynamically segmented according to the R-R interval to obtain different lengths of heartbeats. The local features of the heartbeats were extracted through the sliding of the convolution kernel of convolution layer, and the average pooling layer performed down-sampling to reduce the dimensionality of the features. The spatial pyramid pooling (SPP) layer extracted the beat features with different pooling sizes. Input features of different lengths were fused by SPP layer to obtain output features of the same length. Extreme learning machine (ELM) as a classifier could improve the speed of classification and shorten the training time. The MIT-BIH arrhythmia database (MITDB) and ten-fold cross-validation method were adopted to complete four classification experiments of arrhythmia. The overall accuracy, sensitivity, specificity, and precision of the classification results in the test set reached 99.16%, 99.85%, 98.89%, and 99.85%, respectively. In the same software environment, the accuracy and training time of hybrid model and single model were verified, and results show that the hybrid model achieved higher accuracy with less training time, which provides a feasible scheme for quickly and accurately identifying the types of arrhythmia.