Paroxysmal Atrial Fibrillation Events Detection from Dynamic ECG Recordings: The 4th China Physiological Signal Challenge 2021

Atrial fibrillation (AF) is the most frequent arrhythmia, but paroxysmal atrial fibrillation (PAF) often remains unrecognized. Early detection of PAF is of great value for AF surgery options, drug intervention and diagnosis and treatment of various clinical complications. Although accurate detection of paroxysmal AF is very important, there is currently no algorithm that can efficiently measure the onset and end of AF episode in dynamic or wearable ECGs. Previous AF detection algorithms usually focus on the classification of AF rhythm instead of locating the onsets and ends of AF episodes. Thus, the clinical significance for the personalized treatment and management of AF patients is limited. The identification of AF rhythm is also influenced by other abnormal rhythms in clinical applications.

The 4th China Physiological Signal Challenge 2021 (CPSC 2021) aims to encourage the development of algorithms for searching the AF episodes in dynamic ECG records. A new dynamic ECG database was constructed to encourage the development of more efficient and robust algorithms for PAF detection. We also develop a new scoring metric to evaluate detection methods for PAF events.

心房颤动（AF）是最常见的节律失常，然而阵发性心房颤动（PAF）往往未能得到识别。PAF的早期检测对于心房颤动手术选择、药物治疗及各种临床并发症的诊断与治疗具有重要意义。尽管准确检测阵发性心房颤动至关重要，但目前尚无算法能够有效测量动态或可穿戴心电图（ECG）中AF发作的开始和结束。既往的心房颤动检测算法通常专注于AF节律的分类，而非AF发作的起始和结束位置的定位。因此，这些算法在针对心房颤动患者的个性化治疗和管理中的临床意义有限。在临床应用中，AF节律的识别也受到其他异常节律的影响。第四届中国生理信号挑战赛2021年（CPSC 2021）旨在鼓励开发算法以搜索动态ECG记录中的AF发作。为促进更高效和鲁棒的PAF检测算法的发展，构建了一个新的动态ECG数据库。同时，我们开发了一种新的评分指标，用于评估PAF事件的检测方法。

搜集汇总

背景与挑战

背景概述

该数据集是用于阵发性房颤(PAF)检测算法开发的ECG数据库，包含来自不同监测设备的动态心电图记录，采样率为200Hz。数据集提供了来自房颤和非房颤患者的两阶段训练数据(共1436条记录)，并采用新的评分标准来评估PAF事件检测算法的性能。