学龄儿童12导联心电图深度学习分析：概念验证研究数据表1

数据集获取（Figshare）

• 源站标题：Data Sheet 1_Deep learning-based analysis of 12-lead electrocardiograms in school-age children: a proof of concept study.xlsx
• DOI：10.3389/fcvm.2025.1471989.s001
• 发布日期：2025-03-05
• 文件数：1 个
• 文件总体积：23.39 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Deep_learning-based_analysis_of_12-lead_electrocardiograms_in_school-age_children_a_proof_of_concept_study_xlsx/28538660

命令行下载

curl -L -o "Data Sheet 1_Deep learning-based analysis of 12-lead electrocardiograms in school-age children: a proof of concept study.xlsx" "https://ndownloader.figshare.com/files/52802564"

文件清单

• Data Sheet 1_Deep learning-based analysis of 12-lead electrocardiograms in school-age children: a proof of concept study.xlsx（23.39 KB）

文件总体积 23.39 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

IntroductionThe diagnostic performance of automated analysis of electrocardiograms for screening children with pediatric heart diseases at risk of sudden cardiac death is unknown. In this study, we aimed to develop and validate a deep learning-based model for automated analysis of ECGs in children.
MethodsWave data of 12-lead electrocardiograms were transformed into a tensor sizing 2 × 12 × 400 using signal processing methods. A deep learning-based model to classify abnormal electrocardiograms based on age, sex, and the transformed wave data was developed using electrocardiograms performed in patients at the age of 6–18 years during 2003–2006 at a tertiary referral hospital in Japan. Eighty-three percent of the patients were assigned to a training group, and 17% to a test group. The diagnostic performance of the model and a conventional algorithm (ECAPS12C, Nihon Kohden, Japan) for classifying abnormal electrocardiograms were evaluated using the cross-tabulation, McNemar’'s test, and decision curve analysis.
ResultsWe included 1,842 ECGs performed in 1,062 patients in this study, and 310 electrocardiograms performed in 177 patients were included in the test group. The specificity of the deep learning-based model for detecting abnormal electrocardiograms was not significantly different from that of the conventional algorithm. For detecting electrocardiograms with ST-T abnormality, complete right bundle branch block, QRS axis abnormality, left ventricular hypertrophy, incomplete right bundle branch block, WPW syndrome, supraventricular tachyarrhythmia, and Brugada-type electrocardiograms, the specificity of the deep learning-based model was higher than that of the conventional algorithm at the same sensitivity.
ConclusionsThe present new deep learning-based method of screening for abnormal electrocardiograms in children showed at least a similar diagnostic performance compared to that of a conventional algorithm. Further studies are warranted to develop an automated analysis of electrocardiograms in school-age children.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcvm.2025.1471989.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Deep_learning-based_analysis_of_12-lead_electrocardiograms_in_school-age_children_a_proof_of_concept_study_xlsx/28538660）。