---
license: apache-2.0
library_name: pytorch
tags:
  - seizure-detection
  - medical-imaging
  - cnn
  - healthcare
  - eeg
pipeline_tag: image-classification
---

# SeizureDetectionCNN

## Model Description

SeizureDetectionCNN is a convolutional neural network designed for binary classification of seizure events using EEG data converted to images. The model employs a simple yet effective architecture with two convolutional layers followed by batch normalization and three fully connected layers.

### Model Architecture

```python
SeizureDetectionCNN(
  (conv1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0)
  (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (bn1): BatchNorm2d(32)
  (bn2): BatchNorm2d(64)
  (dropout): Dropout(p=0.5)
  (fc1): Linear(in_features=4096, out_features=120)
  (fc2): Linear(in_features=120, out_features=32)
  (fc3): Linear(in_features=32, out_features=2)
)
```

### Input Description

Input images are preprocessed to 32x32 grayscale
Images are normalized with mean=[0.5] and std=[0.5]
Input tensor shape: (batch_size, 1, 32, 32)

### Preprocessing
```python
from torchvision import transforms

transforms.Compose([
    transforms.Grayscale(),
    transforms.Resize((32, 32)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5], std=[0.5])
])
```

## Training Procedure

### Architectural Features

2 Convolutional layers with ReLU activation
Batch Normalization after each convolutional layer
MaxPooling with kernel size 2
Dropout (p=0.5) for regularization
3 Fully connected layers

### Parameters

Total Parameters: ~500K
Input Channels: 1 (grayscale)
Output Classes: 2 (binary classification)

## Intended Uses & Limitations
### Intended Uses

Research and development in seizure detection
Processing of EEG data converted to image format
Binary classification of seizure/non-seizure events