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.

本由查普曼大学、绍兴人民医院（浙江大学医学院绍兴医院）及宁波第一医院共同发起的全新心电图（ECG）信号研究数据库，旨在助力科学界开展关于心律失常及其他心血管疾病的新研究。诸如心房颤动等特定类型的心律失常对公共卫生、生活质量及医疗开支产生显著的负面影响。作为一种无创检测手段，心电图是检测此类疾病的主要且至关重要的诊断工具。然而，这一实践产生了大量数据，其分析需要由专家投入大量的时间和精力。现代机器学习和统计工具能够在高质量、大规模数据集上进行训练，以达到卓越的自动化诊断准确度。因此，我们收集并传播了包含45,152位患者12导联心电图，采样率为500Hz，并具有多种常见心律及额外心血管状况的数据集。该数据集可用于设计、比较和微调针对心律失常及其他心血管疾病的研究中的新型及经典统计和机器学习技术。

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，专为心律失常研究设计，包含45,152名患者的专家标注数据，采样率为500Hz，涵盖多种常见心律和心血管状况。它由Chapman大学、绍兴市人民医院和宁波第一医院合作创建，旨在支持机器学习和统计方法在心血管疾病自动化诊断中的开发与优化。