Nightingale 开放科学数据集
该数据集是一个包含心电图波形和心脏超声影像的多模态医学数据集，通过将ECG信号与超声心动图数据进行对齐匹配，支持早期无症状心脏病的检测研究，可用于心血管疾病风险预测、多模态医学数据分析以及机器学习模型在心脏病诊断领域的应用开发。
基本信息
资源简介
Nightingale 开放科学数据集
【数据集背景】
本数据集源站标题为《kris96tian/ECG_Data_DeepLearning》。源站首次发布：2023-07-31。
【数据内容】
源站原始描述：Diagnosing ‘silent’ heart attack using ECG waveforms (A Nightingale Open Science dataset) | Real dataset trained using CNNs, RNNs etc., including web-app # Diagnosing ‘silent’ heart attacks using ECG waveforms # ## Wb-App: # ## Abstract This project focuses on the early detection of silent heart attacks, one of the most pressing health concerns, through the innovative application of machine learning models to analyze electrocardiogram (ECG) waveforms. Silent heart attacks are a type of myocardia…
【主题与分类】
主题标签：cnn-pytorch、ecg-classification、pytorch-implementation、rnn-pytorch。
【获取与许可】
源站页面：https://github.com/kris96tian/ECG_Data_DeepLearning
【使用注意】
· 源站最后修改于 2025-01-19，引用时请注意版本时效。
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取
git clone https://github.com/kris96tian/machine_learning_ecg.git
curl -L -o repo.zip https://github.com/kris96tian/machine_learning_ecg/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录（使用方式）
Diagnosing ‘silent’ heart attacks using ECG waveforms
Wb-App: https://huggingface.co/spaces/kral2796/heart-attack-prediction
Abstract
This project focuses on the early detection
of silent heart attacks, one of the most
pressing health concerns, through the
innovative application of machine learning
models to analyze electrocardiogram (ECG)
waveforms. Silent heart attacks are a type
of myocardial infarction that often go
undiagnosed due to their asymptomatic
nature, posing a significant health risk due
to the delay in intervention and treatment
which, consequently, leads to a higher risk
of heart failure. The comprehensive
Nightingale Open Science dataset, a
meticulously curated dataset that aligns
ECG waveforms with cardiac ultrasound
data, served as the primary data source for
this project. The primary objective of the
study was the identification of Regional Wall
Motion Abnormalities (RWMA), which are
key indicators of prior myocardial
infarctions. Various machine learning
nmethodologies, including the more
traditional Logistic Regression and Support
Vector Machine (SVM), as well as the
state-of-the-art Recurrent Neural Network
(RNN), were evaluated in this research. The
results demonstrated the immense potential
of machine learning models in the detection
of silent heart attacks through the analysis
of ECG waveforms. Among the models
evaluated, the RNN model stood out as it
outperformed the other models in detecting
RWMA due to its innate ability to capture
sequential and time-dependent data.
However, it is important to note that
challenges such as data imbalance and
overfitting were encountered during this
study, highlighting the need for further
refinement and optimization of the model.
References
Pramanik, Rajiv, Bhumil Shah, Anna Roth, Honga Wei, Ted Castillo, Katie Lin, Sachin Shah, et al.
“Diagnosing ’Silent’ Heart Attack Using ECG Waveforms.”
Nightingale Open Science, 2021. https://doi.org/10.48815/N54W2V.
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。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
⚡️ 需要数据支持或标注服务？立即联系我们获取专业方案。
