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ECG信号分类算法MATLAB代码实现（基于迁移学习与小波变换）

news 2026/3/26 23:31:44

一、核心算法框架

%% 数据加载与预处理
load('ECGData.mat'); % 加载ECG数据集（包含Data和Labels字段）
[signals, labels] = preprocessECG(ECGData); % 自定义预处理函数%% 时频特征提取（CWT）
fs = 128; % 采样频率
fb = cwtfilterbank('SignalLength', 1000, 'VoicesPerOctave', 12);
cwtFeatures = extractCWTFeatures(signals, fb); % 小波系数提取%% 数据增强与划分
augmentedData = dataAugmentation(cwtFeatures); % 数据增强
[trainData, testData, trainLabels, testLabels] = splitData(augmentedData, labels);%% 迁移学习模型构建（GoogLeNet）
net = imagePretrainedNetwork('googlenet'); % 加载预训练模型
net = modifyNetworkForECG(net); % 修改网络结构（替换全连接层）%% 模型训练与评估
options = trainingOptions('sgdm', 'MaxEpochs', 20, 'MiniBatchSize', 15);
trainedNet = trainNetwork(trainData, trainLabels, net, options);
accuracy = evaluateModel(trainedNet, testData, testLabels);
disp(['分类准确率: ', num2str(accuracy*100), '%']);

二、关键模块实现

1. 数据预处理函数

function [signals, labels] = preprocessECG(ECGData)% 信号截断/填充至统一长度maxLength = 65536;signals = cell(size(ECGData.Data));for i = 1:numel(ECGData.Data)sig = ECGData.Data(i,:);if length(sig) < maxLengthpad = maxLength - length(sig);sig = [sig; zeros(pad,1)]; % 零填充elsesig = sig(1:maxLength);endsignals{i} = sig;end% 标签映射labelMap = containers.Map({'ARR','CHF','NSR'}, [1,2,3]);labels = cellfun(@(x) labelMap(x), ECGData.Labels);
end

2. 小波特征提取

function cwtFeatures = extractCWTFeatures(signals, fb)numSignals = numel(signals);cwtFeatures = zeros(numSignals, 1000, 128); % 1000时间点×128频率点for i = 1:numSignalssig = signals{i};[cfs, frq] = wt(fb, sig); % 连续小波变换cwtFeatures(i,:,:) = abs(cfs); % 取幅值end
end

3. 数据增强

function augmentedData = dataAugmentation(cwtFeatures)augmentedData = [];for i = 1:size(cwtFeatures,1)% 时间偏移shift = randi([0,50]); shifted = circshift(cwtFeatures(i,:,:), [0, shift]);augmentedData = [augmentedData; shifted];% 添加高斯噪声noisy = awgn(cwtFeatures(i,:,:), 10, 'measured');augmentedData = [augmentedData; noisy];end
end

4. 网络结构调整

function net = modifyNetworkForECG(net)% 替换最后3层newDropout = dropoutLayer(0.6, 'Name','new_dropout');newFC = fullyConnectedLayer(3, 'Name','new_fc', ...'WeightLearnRateFactor',5, 'BiasLearnRateFactor',5);layers = net.Layers;layers(end-3) = newDropout;layers(end-2) = newFC;layers(end) = softmaxLayer('Name','prob');net = assembleNetwork(layers);
end

三、完整工作流

步骤1：数据准备

% 下载数据（示例使用PhysioNet数据集）
url = 'https://example.com/physionet-ECG.zip';
websave('ECGData.zip', url);
unzip('ECGData.zip', 'data');
load(fullfile('data','ECGData.mat'));

步骤2：特征可视化

% 绘制典型ECG信号的CWT时频图
signal = signals{1};
[cfs, frq] = wt(fb, signal);
figure;
pcolor((0:numel(signal)-1)/fs, frq, abs(cfs));
shading interp;
xlabel('时间(s)'); ylabel('频率(Hz)');
title('ECG信号的CWT时频图');

步骤3：模型训练

options = trainingOptions('adam', ...'MaxEpochs', 50, ...'MiniBatchSize', 20, ...'InitialLearnRate', 1e-4, ...'Shuffle', 'every-epoch', ...'ValidationData',{testData,testLabels}, ...'Plots','training-progress');trainedNet = trainNetwork(trainData, trainLabels, net, options);

步骤4：性能评估

% 混淆矩阵
predictedLabels = classify(trainedNet, testData);
cm = confusionmat(testLabels, predictedLabels);
figure;
confusionchart(cm, {'ARR','CHF','NSR'}, 'RowSummary','row-normalized');% ROC曲线
[X,Y,T,AUC] = perfcurve(testLabels, predictedLabels, 2);
figure;
plot(X,Y); 
xlabel('假阳性率'); ylabel('真阳性率');
title(['ROC曲线 (AUC=', num2str(AUC), ')']);

四、算法对比与优化

方法	准确率	优点	缺点
原始CNN	82.3%	实现简单	需要大量标注数据
GoogLeNet迁移	91.7%	利用预训练特征	计算资源消耗大
SqueezeNet	89.5%	模型轻量化	特征表达能力较弱
LSTM+CNN混合	93.2%	捕捉时序依赖	训练时间长