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model.py

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+import torch
+import torch.nn as nn
+
+class CardiovascularRNN(nn.Module):
+    def __init__(self, input_size, hidden_size, num_layers, num_classes):
+        super(CardiovascularRNN, self).__init__()
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
+        
+        # RNN layer (using GRU for better performance over plain RNN)
+        self.gru = nn.GRU(input_size, hidden_size, num_layers, batch_first=True)
+        
+        # Fully connected layer
+        self.fc = nn.Linear(hidden_size, num_classes)
+        
+    def forward(self, x):
+        # Initialize hidden state
+        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
+        
+        # Forward propagate GRU
+        out, _ = self.gru(x, h0)
+        
+        # Decode the hidden state of the last time step
+        out = out[:, -1, :]
+        
+        # Output layer
+        out = self.fc(out)
+        return out