eeg-dementia-classification-MNet 1.4.1

Pretrained MNet model for classifying demetia subclasses (HV, AD, DLB, and iNPH)

Installation

$ pip install eeg-dementia-classification-MNet

Pretrained Weights

Pretrained weights are available on our Google Drive.

1. Download 'pretrained_weights.tar.gz'.
2. Extract the file using the following command:

$ tar xvf pretrained_weights.tar.gz

3. Locate the extradcted 'pretrained_weights' directory at the working directory. As an illustration, the weight files (.pth) should be organized as follows:

./pretrained_weights/
├── AD_vs_DLB
│   ├── model_fold#0_epoch#045.pth
│   ├── model_fold#1_epoch#031.pth
│   ├── model_fold#2_epoch#029.pth
│   ├── model_fold#3_epoch#031.pth
│   └── model_fold#4_epoch#028.pth
├── AD_vs_DLB_vs_NPH
│   ├── model_fold#0_epoch#024.pth
│   ├── model_fold#1_epoch#035.pth
...

Usage

from eeg_dementia_classification_MNet import MNet_1000
import torch

## Parameters
DISEASE_TYPES = ["HV", "AD", "DLB", "NPH"]

## MNet
model = MNet_1000(DISEASE_TYPES, is_ensemble=True)
model.load_weights(i_fold=0)

## Feeds data
bs, n_chs, seq_len = 16, 19, 1000
x = torch.rand(bs, n_chs, seq_len)
y = model(x)

Contact

Please feel free to contact the author.