TSCNN:面向可穿戴心电信号监测与分析的卷积神经网络  

TSCNN: a convolutional neural network for the monitoring and analysis of the electrical signals of the heart from wearable devices

作　　者：孟琭[1] 葛康 宋阳 杨东溟 Meng Lu;Ge Kang;Song Yang;Yang Dongming(College of Information Science and Engineering,Northeastern University,Shenyang 110819,China;Beijing Irealcare Technology Pte.,Ltd.,Beijing 100000,China;Panjin Central Hospital,Panjin 124000,China)

机构地区：[1]东北大学信息科学与工程学院,沈阳110819 [2]北京瑞尔视景科技有限公司,北京100000 [3]盘锦市中心医院,盘锦124000

出　　处：《中国图象图形学报》2020年第10期2281-2292,共12页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61973058);中央高校基本科研业务费专项资金资助(N2004020)。

摘　　要：目的可穿戴设备能够长时间实时监测人体心脏状况,其在心电信号监测领域应用广泛。但目前仍没有公开的来自可穿戴设备的心电数据集,大部分心电信号分析算法都是针对医院设备所采集的心电数据。因此,本文使用IREALCARE 2.0柔性远程心电贴作为心电信号监测和采集设备制作了可穿戴设备的心电数据集。针对可穿戴心电数据干扰多、数据量大等特点,本文提出了一种针对可穿戴设备获得的心电信号进行自动分类的深层卷积神经网络,称之为时空卷积神经网络(time-spatial convolutional neural networks,TSCNN)。方法将原始的长时间心电信号分割为单个的心搏并与滤波后不同频段的心搏数据组合成十通道的数据输入到TSCNN中。TSCNN对每个心搏使用时间卷积和空间滤波来提取丰富的特征。采用小卷积核级联卷积的方式提高分类性能,并降低网络的参数量和计算量。结果在本文制作的心电数据集上进行了测试,并与其他4种心电分类算法:CNN(convolutional neural networks)、RNN(recurrent neural networks)、1-DCNN(1-dimensional convolution neural networks)和DCN(dense convolutional networks)进行了比较。实验结果显示,本文方法的分类准确率达到91.16%,优于其他4种方法。结论本文方法面向可穿戴心电数据,获得了较好的分类性能,可以有效监控穿戴者是否出现了心电异常情况。Objective Wearable devices are expanding in terms of electrocardiograms(ECG)because they can portably monitor the heart condition of a human for a long time.The key for wearable devices to monitor heart conditions in real time is to be able to process the collected ECG data automatically.Therefore,an efficient and reliable classification algorithm for ECG data from wearable devices should be designed.Many ECG classification algorithms have been proposed in recent years.They are mainly divided into two categories:one is based on handcrafted features,and the other is based on deep learning.Classification methods based on artificial features need to design various features manually,and many useful features are often ignored.The deep learning method can automatically extract features,and it has achieved good performance in image classification,object recognition,and natural language processing.However,no ECG data set from wearable devices is publicly available.Most ECG analysis algorithms are aimed at ECG data collected using hospital equipment.The ECG signal collected using wearable devices is susceptible to various interferences due to the body movement of people and changes in the surrounding environment,resulting in various noises in the signal.Therefore,many methods that perform well in ECG data sets collected using hospital equipment cannot achieve accurate classification of ECG data from wearable devices.In this study,we used IREALCARE 2.0 flexible cardiac monitor patch as the wearable device to collect ECG signals and make ECG data sets.It has the characteristics of small size,lightweight,and accurate and reliable measurement.The ECG data set came from 38 subjects and mainly included five types of heartbeats:normal,ventricular premature beat(VPB),supraventricular premature beat(SPB),atrial fibrillation(AF),and interference.In accordance with the characteristics of ECG data from wearable devices,such as considerable interference and large amount of data,this study proposed a deep convolutional neural network(C

关 键 词：可穿戴设备 可穿戴心电数据集 心脏监测 卷积神经网络 空间滤波 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]