A large scale 12-lead electrocardiogram database for arrhythmia study

This newly inaugurated research database for 12-lead electrocardiogram (ECG)
signals was created under the auspices of Chapman University, Shaoxing
People's Hospital (Shaoxing Hospital Zhejiang University School of Medicine),
and Ningbo First Hospital. It aims to enable the scientific community in
conducting new studies on arrhythmia and other cardiovascular conditions.
Certain types of arrhythmias, such as atrial fibrillation, have a pronounced
negative impact on public health, quality of life, and medical expenditures.
As a non-invasive test, ECG is a major and vital diagnostic tool for detecting
these conditions. This practice, however, generates large amounts of data, the
analysis of which requires considerable time and effort by human experts.
Modern machine learning and statistical tools can be trained on high quality,
large data to achieve exceptional levels of automated diagnostic accuracy.
Thus, we collected and disseminated this novel database that contains 12-lead
ECGs of 45,152 patients with a 500 Hz sampling rate that features multiple
common rhythms and additional cardiovascular conditions, all labeled by
professional experts. The dataset can be used to design, compare, and fine-
tune new and classical statistical and machine learning techniques in studies
focused on arrhythmia and other cardiovascular conditions.

本全新启用的12导联心电图（12-lead electrocardiogram, ECG）信号研究数据库，由查普曼大学、绍兴市人民医院（浙江大学医学院附属绍兴医院）及宁波市第一医院联合创建。
本数据库旨在为科学界开展心律失常及其他心血管疾病相关研究提供支撑。
部分心律失常类型（例如心房颤动）会对公众健康、生活质量及医疗开支造成显著负面影响。
心电图作为无创检查手段，是检测此类疾病的核心关键诊断工具，但该检查会产生海量数据，其分析工作需要临床专家投入大量时间与精力。
现代机器学习与统计分析工具可通过高质量大规模数据进行训练，从而实现极高水平的自动化诊断准确率。
为此，本团队收集并发布了这款新型数据库：该库包含45152名患者的12导联心电图数据，采样率为500赫兹，涵盖多种常见心律及其他心血管疾病类型，所有标注均由专业专家完成。
本数据集可用于设计、对比及微调新型与经典统计及机器学习方法，以支撑心律失常及其他心血管疾病相关研究。

搜集汇总

背景与挑战

背景概述

该数据集是一个大规模12导联心电图数据库，专门用于心律失常研究，包含45,152名患者的ECG信号，采样率为500 Hz，并由专业专家标注了多种常见心律和心血管状况。数据以CSV和WFDB格式提供，适用于机器学习和统计方法开发，旨在支持心律失常自动诊断算法的训练和验证。