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.本新建的12导联心电图（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共建。本数据库旨在为科研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（如心房颤动）会对公众健康、生活质量及医疗支出造成显著负面影响。作为一种无创检查手段，心电图是检测此类心血管疾病的核心且关键的诊断工具。但该检查会产生海量数据，其分析工作需要专业医护人员投入大量时间与精力。依托高质量大规模数据训练的现代机器学习与统计分析工具，可实现极高水平的自动化诊断准确率。为此，我们收集并发布了这款新型数据库：其中包含45152名患者的12导联心电图数据，采样率为500Hz，涵盖多种常见心律类型及其他心血管疾病状态，所有标签均由专业临床专家标注。该数据集可用于设计、对比并微调新型与经典的统计分析及机器学习方法，服务于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，包含45,152名患者的专业标注心电图记录，采样率为500 Hz，旨在支持心律失常和心血管疾病的研究。数据涵盖多种常见心律和疾病类型，如心房颤动和室性早搏，适用于机器学习和统计方法的训练与验证，以提升自动诊断的准确性。