基于金字塔卷积结构的深度残差网络心电信号分类方法研究

Research on electrocardiogram classification using deep residual network with pyramid convolution structure

近年来,深度神经网络(DNNs)已广泛应用于心电图(ECG)信号分类领域,但是以往的模型从原始ECG数据中提取特征信息受限。因此,本文提出了一种基于金字塔型卷积层的深度残差网络(PC-DRN)算法,该算法中包含的金字塔型卷积(PC)层可以从原始ECG数据中同时提取多尺度特征,并采用深度残差网络训练ECG信号分类模型,可以实现对ECG信号的分类。本文使用2017心脏病学挑战赛(CinC2017)提供的公开数据集,验证本文提出方法对4类ECG数据的分类效果。本文选取精度和召回率之间的谐波均值F1作为主要评价指标。实验结果表明,PC-DRN的平均序列级别F1(SeqF1)从0.857提升到了0.920,平均集合级别F1(SetF1)从0.876提升到了0.925。因此,本文提出的PC-DRN算法为ECG信号的特征提取和分类提供了一种新的思路,为心律失常的分类诊断提供了有效的手段。

Recently,deep neural networks(DNNs)have been widely used in the field of electrocardiogram(ECG) signal classification,but the previous models have limited ability to extract features from raw ECG data.In this paper,a deep residual network model based on pyramidal convolutional layers(PC-DRN)was proposed to implement ECG signal classification.The pyramidal convolutional(PC)layer could simultaneously extract multi-scale features from the original ECG data.And then,a deep residual network was designed to train the classification model for arrhythmia detection.The public dataset provided by the physionet computing in cardiology challenge 2017(CinC2017)was used to validate the classification experiment of 4 types of ECG data.In this paper,the harmonic mean F1 of classification accuracy and recall was selected as the evaluation indexes.The experimental results showed that the average sequence level F1(SeqF1)of PCDRN was improved from 0.857 to 0.920,and the average set level F1(SetF1)was improved from 0.876 to 0.925.Therefore,the PC-DRN model proposed in this paper provided a promising way for the feature extraction and classification of ECG signals,and provided an effective tool for arrhythmia classification.