Detecting and Quantifying Apnea Based on the ECG: The PhysioNet/Computing in Cardiology Challenge 2000

Obstructive sleep apnea (intermittent cessation of breathing) is a common problem with major health implications, ranging from excessive daytime drowsiness to serious cardiac arrhythmias. Obstructive sleep apnea is associated with increased risks of high blood pressure, myocardial infarction, and stroke, and with increased mortality rates. Standard methods for detecting and quantifying sleep apnea are based on respiration monitoring, which often disturbs or interferes with sleep and is generally expensive. A number of studies during the past 15 years have hinted at the possibility of detecting sleep apnea using features of the electrocardiogram. Such approaches are minimally intrusive, inexpensive, and may be particularly well-suited for screening. The major obstacle to use of such methods is that careful quantitative comparisons of their accuracy against that of conventional techniques for apnea detection have not been published.

We therefore offer a challenge to the biomedical research community: demonstrate the efficacy of ECG-based methods for apnea detection using a large, well-characterized, and representative set of data. The goal of the contest is to stimulate effort and advance the state of the art in this clinically significant problem, and to foster both friendly competition and wide-ranging collaborations. We will award prizes of US$500 to the most successful entrant in each of two events.1

阻塞性睡眠呼吸暂停（呼吸间歇性停止）是一种具有重大健康影响的常见问题，其影响范围从白天的过度嗜睡直至严重的心律失常。阻塞性睡眠呼吸暂停与高血压、心肌梗死和中风的风险增加相关，且与死亡率上升有关。目前检测和量化睡眠呼吸暂停的标准方法基于呼吸监测，这种方法往往干扰或妨碍睡眠，且通常成本较高。在过去15年中，多项研究表明，利用心电图特征检测睡眠呼吸暂停具有可能性。此类方法侵入性小、成本低廉，特别适合用于筛查。此类方法应用的主要障碍在于，它们与传统的呼吸暂停检测技术的准确性进行仔细的定量比较尚未发表。因此，我们向生物医学研究界发起挑战：使用一组大型、特征明确且具有代表性的数据集，展示基于心电图方法的呼吸暂停检测的有效性。本次竞赛的目的是激发研究热情，推动该临床重要问题的技术进步，并促进友好竞争和广泛的合作。我们将为每个事件中表现最成功的参赛者颁发500美元的奖金。