摘要
针对传统基于形态特征的心电检测算法存在特征提取不准确和高复杂性等问题,提出了一种多层的长短时记忆(LSTM)神经网络结构。结合传统LSTM模型在时序数据处理上的优势,该模型增加了反向和深度计算,避免了人工提取波形特征,提高了网络的学习能力。通过给定心拍序列和分类标签进行监督学习,然后实现对未知心拍的心律失常检测。通过对MIT-BIH数据库中的心律失常数据集进行实验验证,模型的总体准确率为98.34%。相比支持向量机(SVM),该模型的准确率和F1值均有提高。
Aiming at the problems of inaccurate feature extraction and high complexity of traditional ElectroCardioGram(ECG)detection algorithms based on morphological features,an improved Long Short-Term Memory(LSTM)neural network was proposed.Based on the advantage of traditional LSTM model in time series data processing,the proposed model added reverse and depth calculations which avoids extraction of waveform features artificially and strengthens learning ability of the network.And supervised learning was performed in the model according to the given heart beat sequences and category labels,realizing the arrhythmia detection of unknown heart beats.The experimental results on the arrhythmia datasets in MIT-BIH database show that the overall accuracy of the proposed method reaches 98.34%.Compared with support vector machine,the accuracy and F1 value of the model are both improved.
作者
杨朔
蒲宝明
李相泽
王帅
常战国
YANG Shuo;PU Baoming;LI Xiangze;WANG Shuai;CHANG Zhanguo(School of Computer and Control Engineering,University of Chinese Academy of Sciences,Beijing 100049,China;Shenyang Institute of Computing Technology,Chinese Academy of Sciences,Shenyang Liaoning 110168,China;School of Computer Science and Engineering,Northeastern University,Shenyang Liaoning 110819,China)
出处
《计算机应用》
CSCD
北大核心
2019年第3期930-934,共5页
journal of Computer Applications
关键词
心律失常
心电
长短时记忆神经网络
时序数据
支持向量机
cardiac arrhythmia
ElectroCardioGram(ECG)
Long Short-Term Memory(LSTM)neural network
time series data
Support Vector Machine(SVM)
