CVAE架构与训练超参数

数据集获取（Figshare）

• 源站标题：CVAE architecture and training hyperparameters.
• DOI：10.1371/journal.pone.0335364.t003
• 发布日期：2025-10-31
• 文件数：1 个
• 文件总体积：5.63 KB
• 源站页面：https://plos.figshare.com/articles/dataset/CVAE_architecture_and_training_hyperparameters_/30503623

命令行下载

curl -L -o "Table 3.xls" "https://ndownloader.figshare.com/files/59215411"

文件清单

• Table 3.xls（5.63 KB）

文件总体积 5.63 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Machine learning (ML) offers great potential in healthcare, especially in the analysis of complex physiological signals like electroencephalography (EEG). EEG recordings hold valuable insights into neurological function and can aid in diagnosing various conditions. In this work, we explore the use of a Conditional Variational Autoencoder (CVAE) that injects a binary health label (healthy or orthopedic impairment) into both the encoder input and the latent space coupled with the extracted features, leveraging the conditional input vector to learn representations specific to different health conditions. Our study involved using two public OpenNeuro datasets [1,2]. From the healthy dataset we randomly selected seven subjects to match the seven impaired participants; in both sets we retained the same 11 scalp channels (C3, Cz, C4, FC3, FCz, FC4, CP3, CPz, CP4, F3, F4). Six descriptors-Short time Fourier Transform (STFT), Hurst Exponent (HE), Detrended Fluctuation Analysis (DFA), Correlation Dimension (CD), Kolmogorov-Sinai Entropy (permutation entropy; KS-proxy), and the Largest Lyapunov Exponent (LLE)-are extracted channel-wise and concatenated to form the input feature vector, which distills distinct characteristics from the EEG signals. We rigorously evaluated the performance of our CVAE model in combination with each feature extraction technique. The conditional supply of class labels to both encoder and decoder enabled the CVAE to achieve 93% accuracy on the unseen test split of the dataset with precision of 93%, a recall of 93%, and an F1-score of 0.93 outperforming re-trained CNN baselines. These results highlight the promise of CVAEs and the significance of well-suited feature extraction for robust EEG classification. This work could contribute to the development of automated healthcare diagnostic tools.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0335364.t003（source: https://plos.figshare.com/articles/dataset/CVAE_architecture_and_training_hyperparameters_/30503623）。