In this work, we present a novel trajectory comparison algorithm to identify abnormal vital sign trends, with the aim of improving recognition of deteriorating health. There is growing interest in continuous wearable vital sign sensors for monitoring patients remotely at home. These monitors are usually coupled to an alerting system, which is triggered when vital sign measurements fall outside a predefined normal range. Trends in vital signs, such as increasing heart rate, are often indicative of deteriorating health, but are rarely incorporated into alerting systems. We introduce a dynamic time warp distance-based measure to compare time series trajectories. We split each multi-variable sign time series into 180 minute, non-overlapping epochs. We then calculate the distance between all pairs of epochs. Each epoch is characterized by its mean pairwise distance (average link distance) to all other epochs, with clusters forming with nearby epochs. We demonstrate in synthetically generated data that this method can identify abnormal epochs and cluster epochs with similar trajectories. We then apply this method to a real-world data set of vital signs from 8 patients who had recently been discharged from hospital after contracting COVID-19. We show how outlier epochs correspond well with the abnormal vital signs and identify patients who were subsequently readmitted to hospital.

翻译：本文提出了一种新颖的轨迹比较算法，用于识别异常生命体征趋势，旨在提升对健康状况恶化的识别能力。目前，可穿戴连续生命体征传感器在居家远程监护中的应用日益受到关注。这类监测设备通常与警报系统联动，当生命体征测量值超出预设的正常范围时触发警报。然而，生命体征趋势（如心率持续上升）常预示病情恶化，却较少被纳入警报系统。我们引入了一种基于动态时间规整距离的度量方法，用于比较时间序列轨迹。将每条多变量体征时间序列划分为180分钟的非重叠时段，计算所有时段对之间的距离，并通过每个时段与其他所有时段之间的平均链接距离来表征该时段，使相似轨迹的时段自然聚类。在合成数据上的实验表明，该方法能有效识别异常时段并对相似轨迹进行聚类。随后，我们将该方法应用于8例COVID-19患者出院后的真实生命体征数据集，证明了离群时段与异常生命体征的高度一致性，并成功识别出后续再度入院的患者。