表2_基于可穿戴心电图的人工智能预测持续性心房颤动患者心力衰竭进展：简要研究报告

数据集获取（Figshare）

• 源站标题：Table 2_AI-based prediction of heart failure progression in persistent atrial fibrillation using wearable electrocardiography: a brief research report.xlsx
• DOI：10.3389/fcvm.2025.1748673.s001
• 发布日期：2026-02-05
• 文件数：1 个
• 文件总体积：9.46 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_2_AI-based_prediction_of_heart_failure_progression_in_persistent_atrial_fibrillation_using_wearable_electrocardiography_a_brief_research_report_xlsx/31267162

命令行下载

curl -L -o "Table 2_AI-based prediction of heart failure progression in persistent atrial fibrillation using wearable electrocardiography: a brief research report.xlsx" "https://ndownloader.figshare.com/files/61664995"

文件清单

• Table 2_AI-based prediction of heart failure progression in persistent atrial fibrillation using wearable electrocardiography: a brief research report.xlsx（9.46 KB）

文件总体积 9.46 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

BackgroundPersistent atrial fibrillation (AF) frequently coexists with heart failure (HF), yet HF monitoring remains limited by the need for repeated blood-based biomarkers such as N-terminal pro-brain natriuretic peptide (NT-proBNP). Advances in wearable electrocardiography (ECG) and artificial intelligence (AI) now allow continuous extraction of digital physiologic signatures that may reflect hemodynamic stress.
ObjectiveTo evaluate the feasibility of predicting HF progression using wearable ECG–derived features in patients with persistent AF.
MethodsFifty patients with persistent AF underwent 3–7 days of single-lead ECG monitoring. Heart rate variability (HRV) and RR-interval features from 30 min windows were combined with baseline clinical metrics. A context-aware deep learning model using long short-term memory (LSTM) and attention mechanisms was trained to predict 6–12-month NT-proBNP changes. Model performance was assessed using root mean squared error (RMSE), mean absolute error (MAE), and the accuracy of directional NT-proBNP change.
ResultsThe best performance was achieved when clinical metrics, RR features, and long-term HRV summaries were combined (RMSE 1,667.04; MAE 950.52). Directional classification of NT-proBNP trajectories achieved an accuracy of 0.82. ECG-only models performed comparably to multimodal models.
ConclusionWearable ECG–based AI modeling is feasible for predicting trends in HF biomarkers in persistent AF. These results provide early evidence that ECG-derived digital biomarkers may offer a scalable, non-invasive approach for longitudinal HF monitoring.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcvm.2025.1748673.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_2_AI-based_prediction_of_heart_failure_progression_in_persistent_atrial_fibrillation_using_wearable_electrocardiography_a_brief_research_report_xlsx/31267162）。