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⛄ 内容介绍
为提高心拍的分类效果,研究基于双向长短期记忆(BiLSTM)模型的深度学习算法.首先,采用"双斜率"法对心电信号进行预处理;然后,设计自适应阈值对预处理后的心电信号进行QRS波定位,并依据R波波峰分割截取心拍;最后,采用BiLSTM模型的深度学习算法对获取的心拍形态进行分类.使用MIT-BIH心率失常数据库验证算法有效性,实验结果表明:文中算法对正常或束支传导阻滞(N),室上性异常(S),心室异常(V),融合(F)类型的敏感性分别为98.56%,97.10%,93.33%,79.52%,特异性分别为98.38%,98.08%,98.54%,99.65%;与传统的支持向量机等方法相比,文中算法能够进一步提高心拍分类的正确率.
⛄ 部分代码
%---------------------------------------------------------------------------------------------------------------------
% Copyright (c) 2019 Kyriaki Kostoglou
% PhD Supervisor: Georgios Mitsis, Associate Professor, Bioengineering Department, McGill University, Montreal, Canada
% Biosignals and Systems Analysis Lab
% Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the % % "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, % distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to % the following conditions:
% The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
% The Software is provided "as is", without warranty of any kind.
%---------------------------------------------------------------------------------------------------------------------
%Estimate TV covariance of the MVAR residuals using GARCH models (see Eq.22)
function results=estimate_GARCH(e,metric,ignore,pmax,S)
M=size(e,1);
for m=1:M
%Testing different GARCH model orders for time-series m
count=0;
md=[];
aic=[];
bic=[];
for q=0:pmax
for r=1:pmax
flag=0;
fprintf('\nFitting GARCH to time-series %d - Testing model orders: [q=%d,r=%d] ',m,q,r)
Mdl = garch(q,r);
try
%The following command may return the following
%error:"Non-zero degree P requires a non-zero degree Q." is
%there is no underlying true heteroskedasticity. Therefore
%we try to catch the error.
[EstMdl,EstParamCov,logL] = estimate(Mdl,e(m,:)','Display','off');
%If it finally goes through then flag is set to 1
flag=1;
catch
%do nothing
end
if(flag==1)
count=count+1;
md(count,:)=[q r];
[aic(count),bic(count)] = aicbic(logL,q+r,length(e(m,:))); %compute AIC/BIC scores based on the estimated GARCH model
end
end
end
%if aic or bic are nonempty vectors then we select the optimal model order based on the aic or bic scores
%if aic or bic are empty vectors then it means that there is no real
%heteroskedasticity in the data to be GARCH modeled
if(isempty(aic)==0)
if(metric==1)
[mm,ii]=min(aic);
else
[mm,ii]=min(bic);
end
%Find optimal GARCH model orders by selecting the pair [q,r] with the minimum AIC/BIC score
fprintf('\nOptimal model order found: [q=%d,r=%d]\n',md(ii,:))
Mdl = garch(md(ii,1),md(ii,2));
[EstMdl] = estimate(Mdl,e(m,:)');
temp=infer(EstMdl,e(m,:)');
results.EstMdl{m}=EstMdl;
%Save TV variance as predicted by the fitted GARCH model for each residual time-series
results.yGARCH(m,:)=smooth(temp(ignore:end),100); %smooth the garch variance prediction
results.hete(m)=1;
else
fprintf('\nNo heteroskedasticity detected')
results.hete(m)=0;
end
end
%Create diagonal TV covariance of the MVAR residuals
newN=size(results.yGARCH,2);
for k=1:newN
results.SGARCH{k}=zeros(M,M);
for m=1:M
if(results.hete(m)==0)
%if GARCH modeling failed, it means that there is no
%heteroskedasticity in the residuals and we just use the
%estimated variance on the whole residual time-series
results.SGARCH{k}(m,m)=S(m,m);
else
%else we use the predicted GARCH TV variance
results.SGARCH{k}(m,m)=results.yGARCH(m,k);
end
end
end
end
%results is a structure that contains the following:
%results.EstMdl{m}=EstMdl; %optimum GARCH estimated model for the mth time-series residuals
%results.yGARCH(m,:) %predicted TV GARCH variance for the mth time-series residuals
%results.SGARCH{k} %TV GARCH covariance of the residuals at time point k
%results.hete(m) %0 if there is no underlying heteroskedasticity in the data else it is set to 1 and heteroskedasticity was modeled using GARCH models
⛄ 运行结果
⛄ 参考文献
[1]万圣贤, 兰艳艳, 郭嘉丰, et al. 用于文本分类的局部化双向长短时记忆[J]. 中文信息学报, 2017, 31(3):7.
[2]叶良攀. 基于BiLSTM的铁路调度语音识别系统研究[D]. 兰州交通大学.
