心电图数据集描述

数据集获取（Figshare）

• 源站标题：<p>ECG dataset description.</p>
• DOI：10.1371/journal.pone.0334217.t001
• 发布日期：2025-10-17
• 文件数：1 个
• 文件总体积：5.63 KB
• 源站页面：https://plos.figshare.com/articles/dataset/ECG_dataset_description_/30388669

命令行下载

curl -L -o "Table 1.xls" "https://ndownloader.figshare.com/files/58846483"

文件清单

• Table 1.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

In health care, an accurate diagnosis with the help of a data-driven forecasting framework takes the risk factors associated with heart disease. However, building such an effective model using deep learning (DL) methods requires high-quality data, i.e., data free of outliers or anomalies. The current paper proposes a new approach to diagnosing and controlling heart diseases by utilizing a multi-tiered data acquisition model, data pre-processing, feature extraction, and DL. The framework encompasses four types of datasets. The first phase of the proposed methodology consists of data acquisition, while the second phase includes advanced data preprocessing for each data type. In phase three, multi-feature extraction methods are used to extract the features from the dataset. In phase four, a combined feature selection technique of ReliefF and Pearson correlation is used to select the best features. Phase five of the study is the formulation of the CILAD-Net DL model that integrates CNN, Inception Net, LSTM, and Angle DetectNet to accurately detect heart disease. The sixth phase implements Deep Reinforcement Learning (DRL) for nutrition recommendations based on the detected disease, thus improving the treatment individualization. The developed model’s experimental outcomes are validated with other prevailing models in terms of accuracy, recall, hamming loss, and so on. Finally, the outcomes of the proposed model attain the higher accuracy of 0. 998 for the CILAD-Net model, which is significantly better than DenseNet-201 with 0. 988, ANN with 0. 987, KNN with 0. 977, and CL-Net with 0. 984.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0334217.t001（source: https://plos.figshare.com/articles/dataset/ECG_dataset_description_/30388669）。