首页> 外文会议>2018 13th IEEE International Conference on Automatic Face amp; Gesture Recognition >The OBF Database: A Large Face Video Database for Remote Physiological Signal Measurement and Atrial Fibrillation Detection
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The OBF Database: A Large Face Video Database for Remote Physiological Signal Measurement and Atrial Fibrillation Detection

机译:OBF数据库:用于远程生理信号测量和心房颤动检测的大型面部视频数据库

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Physiological signals, including heart rate (HR), heart rate variability (HRV), and respiratory frequency (RF) are important indicators of our health, which are usually measured in clinical examinations. Traditional physiological signal measurement often involves contact sensors, which may be inconvenient or cause discomfort in long-term monitoring sessions. Recently, there were studies exploring remote HR measurement from facial videos, and several methods have been proposed. However, previous methods cannot be fairly compared, since they mostly used private, self-collected small datasets as there has been no public benchmark database for the evaluation. Besides, we haven't found any study that validates such methods for clinical applications yet, e.g., diagnosing cardiac arrhythmias/disease, which could be one major goal of this technology. In this paper, we introduce the Oulu Bio-Face (OBF) database as a benchmark set to fill in the blank. The OBF database includes large number of facial videos with simultaneously recorded reference physiological signals. The data were recorded both from healthy subjects and from patients with atrial fibrillation (AF), which is the most common sustained and widespread cardiac arrhythmia encountered in clinical practice. Accuracy of HR, HRV and RF measured from OBF videos are provided as the baseline results for future evaluation. We also demonstrated that the video-extracted HRV features can achieve promising performance for AF detection, which has never been studied before. From a wider outlook, the remote technology may lead to convenient self-examination in mobile condition for earlier diagnosis of the arrhythmia.
机译:生理信号,包括心率(HR),心率变异性(HRV)和呼吸频率(RF)是我们健康的重要指标,通常在临床检查中进行测量。传统的生理信号测量通常涉及接触式传感器,这在长期监测过程中可能会带来不便或引起不适。最近,有研究探索从面部视频进行远程HR测量的研究,并提出了几种方法。但是,以前的方法无法公平地比较,因为它们大多使用私有的,自我收集的小型数据集,因为还没有用于评估的公共基准数据库。此外,我们尚未找到任何可验证此类方法用于临床的研究,例如,诊断心律不齐/疾病,这可能是这项技术的主要目标。在本文中,我们介绍了Oulu Bio-Face(OBF)数据库作为基准集以填补空白。 OBF数据库包含大量面部视频,同时记录了参考生理信号。记录了健康受试者和房颤(AF)患者的数据,房颤是临床实践中最常见的持续性和广泛性心律失常。从OBF视频中测得的HR,HRV和RF的准确性将作为基线结果,以供将来评估。我们还证明了视频提取的HRV功能可以实现有希望的AF检测性能,这是以前从未研究过的。从更广阔的前景来看,远程技术可能会导致在移动状态下进行方便的自检,以进行心律失常的早期诊断。

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