基于判别式深度置信网络的心律失常自动分类方法

现有的心律失常分类方法通常采用人为选取心电图(ECG)信号特征的方式,其特征选取具有主观性,且特征提取复杂,导致分类准确性容易受到影响等。基于以上问题,本文提出了一种基于判别式深度置信网络(DDBNs)的心律失常自动分类新方法。该方法所构建的生成受限玻尔兹曼机(GRBM)自动提取心拍信号形态特征,然后引入具有特征学习和分类能力的判别式受限玻尔兹曼机(DRBM),依据提取的形态特征和RR间期特征进行心律失常分类。为了进一步提高DDBNs的分类性能,本文将DDBNs转换为使用柔性最大值(Softmax)回归层进行监督分类的深度神经网络(DNN),通过反向传播对网络进行微调。最后,采用麻省理工学院与贝斯以色列医院心律失常数据库(MIT-BIHAR)进行实验验证,对于数据来源一致的训练集和测试集,该方法整体分类精度可达99.84%±0.04%;对于数据来源非一致的训练集和测试集,通过主动学习(AL)方法扩充少量训练集,该方法整体分类精度可达99.31%±0.23%。实验结果表明了该方法在心律失常自动特征提取和分类上的有效性,为深度学习自动提取ECG信号特征及分类提供了一种新的解决方法。

Automatic classification method of arrhythmia based on discriminative deep belief networks

Existing arrhythmia classification methods usually use manual selection of electrocardiogram(ECG)signal features,so that the feature selection is subjective,and the feature extraction is complex,leaving the classification accuracy usually affected.Based on this situation,a new method of arrhythmia automatic classification based on discriminative deep belief networks(DDBNs)is proposed.The morphological features of heart beat signals are automatically extracted from the constructed generative restricted Boltzmann machine(GRBM),then the discriminative restricted Boltzmann machine(DRBM)with feature learning and classification ability is introduced,and arrhythmia classification is performed according to the extracted morphological features and RR interval features.In order to further improve the classification performance of DDBNs,DDBNs are converted to deep neural network(DNN)using the Softmax regression layer for supervised classification in this paper,and the network is fine-tuned by backpropagation.Finally,the Massachusetts Institute of Technology and Beth Israel Hospital Arrhythmia Database(MIT-BIH AR)is used for experimental verification.For training sets and test sets with consistent data sources,the overall classification accuracy of the method is up to 99.84%±0.04%.For training sets and test sets with inconsistent data sources,a small number of training sets are extended by the active learning(AL)method,and the overall classification accuracy of the method is up to 99.31%±0.23%.The experimental results show the effectiveness of the method in arrhythmia automatic feature extraction and classification.It provides a new solution for the automatic extraction of ECG signal features and classification for deep learning.