基本信息

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   源码介绍

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clc;clear;close all
load("C:\Users\admin\Desktop\to_student\Train_data\data_train.mat"); 
L=length(data_train);
data_train(1).length=[];
data_train(2).label2=[];
for  k=1:L
    signal=data_train(k).sequence;
    %对信号进行方差归一化，所有信号的方差都是1，消除信号在幅值上的差异
    signal=signal/std(signal);     
    % 设计带通滤波器去除极限漂移和高频噪声
    fc=[0.5  40];
    [b,a]=butter(3,2*fc/data_train(k,1).fs);
    fsignal=filter(b,a,signal);
    data_train(k,1).sequence=fsignal;
    data_train(k).length=length(data_train(k,1).sequence);
    
       if strcmp(data_train(k,1).label,'ECG')==1
        data_train(k).label2=1;

    elseif  strcmp(data_train(k,1).label,'PPG')==1
        data_train(k).label2=2;
       
    elseif strcmp(data_train(k,1).label,'EEG')==1
       data_train(k).label2=3;
       
       end
end
[xh,I]=sort([data_train.length]);
signal=data_train(I);
XTrain=cell(2474,1);%包含预测变量
YTrain=zeros(1,2474);%包含分类标签或数字响应
for k=1:2474
XTrain{k,1}=(data_train(k).sequence(1:5000))';
YTrain(k)=data_train(k).label2;
end
YTrain=YTrain';
YTrain=categorical(YTrain);
inputSize = 1;
numHiddenUnits = 100;
numClasses = 3;%指定3个类
layers = [ ...
    sequenceInputLayer(inputSize)
    bilstmLayer(numHiddenUnits,'OutputMode','last')
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer]
maxEpochs = 100;%最大纪元数
miniBatchSize = 10;%小批量大小

options = trainingOptions('adam', ...
    'ExecutionEnvironment','auto', ...
    'GradientThreshold',1, ...
    'MaxEpochs',maxEpochs, ...
    'MiniBatchSize',miniBatchSize, ...
    'SequenceLength','longest', ...
    'Shuffle','never', ...
    'Verbose',0, ...
    'Plots','training-progress');
net = trainNetwork(XTrain,YTrain,layers,options);
save('net.mat','net');