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

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：

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`。