SVM支持向量机和逻辑回归进行心音信号简单二分类

# SVM支持向量机和逻辑回归进行心音信号简单二分类 matlab实现：

%%
%本程序读取training-a中150个心音信号作为训练集，后150个心音信号作为测试数据，采用SVM支持向量机和逻辑回归分类器
%进行心音信号的分类
%%
clear;
clc;
rng=[0 1 149 1];%训练 rng=[r1 c1 r2 c2]定义读取csv文件的行列始末段
rng1=[150 1 299 1];%测试
train_label=csvread('./training-a/REFERENCE.csv',0,1,rng);%读取训练标签
for i=1:length(train_label)
    if train_label(i)<0
        train_label(i)=2;
    end
end

test_label=csvread('./training-a/REFERENCE.csv',150,1,rng1);%读取测试标签
for i=1:length(test_label)
    if test_label(i)<0
        test_label(i)=2;
    end
end
figure();
subplot(211);
bar(test_label);%画出测试数据的正确分类标签
%%
%对原始数据进行特征提取，构建n维的训练数据矩阵，样本大小为150
train_data=[];
test_data=[];
for i=1:9
    ss=feature_extraction(strcat('./training-a/a000',num2str(i),'.wav'));
    train_data=[train_data;ss];
end
for i=10:99
    ss=feature_extraction(strcat('./training-a/a00',num2str(i),'.wav'));
    train_data=[train_data;ss];
end
for i=100:150
    ss=feature_extraction(strcat('./training-a/a0',num2str(i),'.wav'));
    train_data=[train_data;ss];
end
%对原始数据进行特征提取，构建n维的测试数据矩阵
for i=151:300
    ss=feature_extraction(strcat('./training-a/a0',num2str(i),'.wav'));
    test_data=[test_data;ss];
end

%%
%SVM支持向量机分类器
% Factor = svmtrain( train_data,train_label);
% predict_label = svmclassify(Factor, test_data);
% accuracy = length(find(predict_label == test_label))/length(test_label)*100;
% accuracy
% subplot(212);
% bar(predict_label);%绘制预测矩阵与正确答案进行比较

%逻辑回归
Factor = mnrfit(train_data, train_label);
Scores = mnrval(Factor, test_data);
S1=Scores(:,1);
S2=Scores(:,2);
predict_label=[];
for i=1:length(S1)
    if S1(i)<S2(i)
        predict_label=[predict_label;2];
    else
        predict_label=[predict_label;1];
    end
end
subplot(212);
bar(predict_label);
accuracy = length(find(predict_label == test_label))/length(test_label)*100;
accuracy
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分类结果：

accuracy =

    76
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由于只采用了150个信号做训练集，训练好的分类器只有76%的预测率，心音信号来源于https://physionet.org/physiobank/database/challenge/2016/training.zip
REFERENCE.csv形式：

特征提取函数feature_extraction采用db6小波特征提取，函数代码如下：

function [ ss ] = feature_extraction(route )
% [x,fs]=audioread('./training-a/a0001.wav');
[x,fs]=audioread(route);
x1=x(:,1); % 抽取第 1 声道
level = 4;
wname ='db6';%选取小波
t=wpdec(x1,level,wname,'shannon');%小波分解
% plot(t);

t0=wprcoef(t,[3,0]);
t1=wprcoef(t,[4,2]);
t2=wprcoef(t,[4,3]);
t3=wprcoef(t,[3,2]);
t4=wprcoef(t,[4,6]);
t5=wprcoef(t,[4,7]);
t6=wprcoef(t,[1,1]);

%构建特征向量
s0=norm(t0);
s1=norm(t1);
s2=norm(t2);
s3=norm(t3);
s4=norm(t4);
s5=norm(t5);
s6=norm(t6);
ss=[s0,s1,s2,s3,s4,s5,s6];%得到7维的特征向量
%  figure();
%  bar(ss);
 end
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转载 原文地址：http://blog.csdn.net/michaelpixer/article/details/54947769