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---
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 |