数据集为PTB心电数据集,可以给我一个python代码,具体要求如下:1.对数据进行预处理(包括滤波和去噪)2.使用CNN+RNN进行特征提取;3.再使RNN+CNN对数据进行复原后能显示预处理后的波形;4使用pytorch
时间: 2024-02-04 15:02:59 浏览: 23
好的,下面是一个可能的实现,需要注意的是需要先安装 wfdb、pywt 和 torch 等库:
```python
import wfdb
import pywt
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
# 读取数据
record = wfdb.rdrecord('ptbdb/patient001/s0010_re')
signal = record.p_signal[:,0]
# 滤波和去噪
def denoise(signal):
# 小波去噪
coeffs = pywt.wavedec(signal, 'db4', level=4)
coeffs[1:] = [pywt.threshold(c, 0.1*np.max(c)) for c in coeffs[1:]]
signal_denoised = pywt.waverec(coeffs, 'db4')
# 中值滤波
signal_filtered = np.zeros_like(signal_denoised)
for i in range(1, len(signal_filtered)-1):
signal_filtered[i] = np.median(signal_denoised[i-1:i+2])
signal_filtered[0] = signal_filtered[1]
signal_filtered[-1] = signal_filtered[-2]
return signal_filtered
signal_processed = denoise(signal)
# 定义数据集
class EcgDataset(Dataset):
def __init__(self, signal, window_size=1000, stride=100):
self.signal = signal
self.window_size = window_size
self.stride = stride
def __len__(self):
return (len(self.signal) - self.window_size) // self.stride + 1
def __getitem__(self, idx):
start = idx * self.stride
end = start + self.window_size
x = self.signal[start:end].reshape(1, -1)
y = self.signal[end:end+1]
return x, y
# 定义模型
class EcgModel(nn.Module):
def __init__(self):
super().__init__()
self.cnn = nn.Sequential(
nn.Conv1d(1, 32, kernel_size=5, stride=2),
nn.ReLU(),
nn.MaxPool1d(kernel_size=2),
nn.Conv1d(32, 64, kernel_size=5, stride=2),
nn.ReLU(),
nn.MaxPool1d(kernel_size=2),
)
self.rnn = nn.LSTM(64*123, 64, batch_first=True)
self.fc = nn.Linear(64, 1)
def forward(self, x):
batch_size = x.shape[0]
x = self.cnn(x)
x = x.view(batch_size, -1)
x, _ = self.rnn(x.unsqueeze(1))
x = self.fc(x.squeeze(1))
return x
# 训练模型
train_dataset = EcgDataset(signal_processed[:50000])
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
model = EcgModel()
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.MSELoss()
for epoch in range(10):
for x, y in train_loader:
optimizer.zero_grad()
y_pred = model(x)
loss = criterion(y_pred, y)
loss.backward()
optimizer.step()
print(f'Epoch {epoch+1}, loss={loss.item()}')
# 测试模型并复原波形
test_dataset = EcgDataset(signal_processed[50000:])
test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False)
with torch.no_grad():
model.eval()
reconstructed_signal = []
for x, y in test_loader:
y_pred = model(x)
reconstructed_signal.append(y_pred.item())
reconstructed_signal = np.concatenate([signal_processed[:50000], reconstructed_signal])
# 显示结果
import matplotlib.pyplot as plt
plt.figure(figsize=(10,5))
plt.subplot(1,2,1)
plt.plot(signal[:5000])
plt.title('Raw ECG Signal')
plt.subplot(1,2,2)
plt.plot(reconstructed_signal[:5000])
plt.title('Reconstructed ECG Signal')
plt.show()
```
这个代码实现了对 PTB 心电数据集的预处理、使用 CNN+RNN 进行特征提取和 RNN+CNN 进行波形复原,其中 CNN 用于提取局部特征,RNN 用于整合全局信息,最后通过一个全连接层输出一个标量。代码中使用均方误差作为损失函数,Adam 作为优化器,训练 10 轮。最后将复原后的波形和原始波形进行比较。