请写一个基于python的利用希尔伯特黄变换利用边际谱熵和一系列特征,再利用pytorch框架的CNN模型对心电信号进行分类的代码
时间: 2023-06-10 20:07:39 浏览: 160
高光谱分类全套代码3D_CNN模型(pytorch)
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以下是一个基于python的利用希尔伯特黄变换利用边际谱熵和一系列特征,再利用pytorch框架的CNN模型对心电信号进行分类的代码:
```python
import numpy as np
import pyhht
from pyhht.visualization import plot_imfs
from pyhht.emd import EMD
from scipy.signal import butter, filtfilt
from entropy import spectral_entropy
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
# Load data
data = np.load("data.npy")
labels = np.load("labels.npy")
# Define function to extract features
def extract_features(signal):
# Apply butterworth bandpass filter
b, a = butter(4, [0.5, 40], btype='band')
filtered_signal = filtfilt(b, a, signal)
# Apply Hilbert-Huang transform
decomposer = EMD(filtered_signal)
imfs = decomposer.decompose()
# Calculate marginal spectral entropy for each IMF
features = []
for i in range(imfs.shape[0]):
features.append(spectral_entropy(imfs[i], sf=1000, method='welch'))
return np.array(features)
# Extract features from data
X = []
for i in range(data.shape[0]):
features = extract_features(data[i])
X.append(features)
X = np.array(X)
# Define CNN model
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv1d(1, 16, 5)
self.pool1 = nn.MaxPool1d(2)
self.conv2 = nn.Conv1d(16, 32, 5)
self.pool2 = nn.MaxPool1d(2)
self.conv3 = nn.Conv1d(32, 64, 5)
self.pool3 = nn.MaxPool1d(2)
self.fc1 = nn.Linear(64*23, 128)
self.fc2 = nn.Linear(128, 64)
self.fc3 = nn.Linear(64, 2)
def forward(self, x):
x = self.pool1(nn.functional.relu(self.conv1(x)))
x = self.pool2(nn.functional.relu(self.conv2(x)))
x = self.pool3(nn.functional.relu(self.conv3(x)))
x = x.view(-1, 64*23)
x = nn.functional.relu(self.fc1(x))
x = nn.functional.relu(self.fc2(x))
x = self.fc3(x)
return x
# Initialize model and optimizer
model = CNN()
optimizer = optim.Adam(model.parameters())
# Define loss function
criterion = nn.CrossEntropyLoss()
# Train model
for epoch in range(100):
running_loss = 0.0
for i in range(X.shape[0]):
# Convert data and labels to PyTorch variables
x = Variable(torch.Tensor(X[i]).view(1, -1, 1))
y = Variable(torch.LongTensor([labels[i]]))
# Zero the parameter gradients
optimizer.zero_grad()
# Forward + backward + optimize
outputs = model(x)
loss = criterion(outputs, y)
loss.backward()
optimizer.step()
# Print statistics
running_loss += loss.data[0]
if i % 100 == 99:
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 100))
running_loss = 0.0
```
注意,这个代码只是一个示例,实际应用中可能需要根据具体情况进行修改。同时,需要安装一些额外的Python库,例如`pyhht`和`entropy`。
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