seizure_detection.py

import torch
import torch.nn as nn
import torch.nn.functional as F


class SeizureDetector(nn.Module):
    def __init__(self, num_classes=2):
        super(SeizureDetector, self).__init__()
        self.conv1= nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1) # 32, 224, 224

        self.pool= nn.MaxPool2d(kernel_size=2, stride=2) # 32, 112, 112

        self.conv2= nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1) # 64, 112, 112 -> 64, 56, 56
        self.conv3= nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1) # 128, 56, 56 -> 128, 28, 28
        self.conv4= nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1) # 256, 28, 28 -> 256, 14, 14

        # Adding Batch Normalization
        self.bn1 = nn.BatchNorm2d(32)
        self.bn2 = nn.BatchNorm2d(64)
        self.bn3 = nn.BatchNorm2d(128)
        self.bn4 = nn.BatchNorm2d(256)

        self.dropout = nn.Dropout(p=0.5)  # Dropout with a probability of 50%

        self.fc1= nn.Linear(256*14*14, 120)
        self.fc2= nn.Linear(120, 32)
        self.fc3= nn.Linear(32, num_classes)

    def forward(self, x):
        x = self.pool(F.relu(self.bn1(self.conv1(x))))  # 32, 112, 112
        x = self.pool(F.relu(self.bn2(self.conv2(x))))  # 64, 56, 56
        x = self.pool(F.relu(self.bn3(self.conv3(x))))  # 128, 28, 28
        x = self.pool(F.relu(self.bn4(self.conv4(x))))  # 256, 14, 14

        x = torch.flatten(x, 1)
        x = self.dropout(F.relu(self.fc1(x)))  # Apply dropout
        x = self.dropout(F.relu(self.fc2(x)))  # Apply dropout
        x = self.fc3(x)
        return x