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 an increasing heart rate, are often indicative of deteriorating health, but are rarely incorporated into alerting systems. In this work, we present a novel outlier detection algorithm to identify such abnormal vital sign trends. We introduce a distance-based measure to compare vital sign trajectories. For each patient in our dataset, we split vital sign time series into 180 minute, non-overlapping epochs. We then calculated a distance between all pairs of epochs using the dynamic time warp distance. Each epoch was characterized by its mean pairwise distance (average link distance) to all other epochs, with large distances considered as outliers. We applied this method to a pilot dataset collected over 1561 patient-hours from 8 patients who had recently been discharged from hospital after contracting COVID-19. We show that outlier epochs correspond well with patients who were subsequently readmitted to hospital. We also show, descriptively, how epochs transition from normal to abnormal for one such patient.
翻译:在家远程监测病人时,人们越来越关注持续磨损关键信号传感器,这些监测器通常与警报系统结合,当重要标志测量在预定正常范围之外时,就触发警报系统。生命迹象的趋势,如心率上升,往往表明健康状况恶化,但很少被纳入警报系统。在这项工作中,我们提出了一个新颖的异端检测算法,以辨别这种异常重要信号趋势。我们采用了远程计量法,以比较关键信号轨迹。对于我们数据集中的每个病人,我们将生命信号时间序列分成180分钟,不重叠的区段。然后我们用动态时间偏移距离计算出所有两对小区之间的距离。每个小区以其平均双向距离(平均连接距离)与所有其他小区之间的特征,而远处被认为是异常的生命迹象趋势。我们用这种方法对从最近从医院出院的8名病人收集的超过1561小时的试验数据集进行了比较。我们用COVID-19来显示,离院的病人的距离很远,我们从一个正常的病人到一个正常的转变。