Objective: Autonomic Dysreflexia (AD) is a potentially life-threatening syndrome which occurs in individuals with higher level spinal cord injuries (SCI). AD is caused by triggers which can lead to rapid escalation of pathophysiological responses and if the trigger is not removed, AD can be fatal. There is currently no objective, non-invasive and accurate monitoring system available to automatically detect the onset of AD symptoms in real time in a non-clinical setting. Technology or Method: We developed a user-independent method of symptomatic AD detection in real time with a wearable physiological telemetry system (PTS) and a machine learning model using data from eleven participants with SCI. Results: The PTS could detect onset of AD symptoms with an average accuracy of 94.10% and a false negative rate of 4.89%. Conclusions: The PTS can detect the onset of the symptoms AD with high sensitivity and specificity to assist people with SCIs in preventing the occurrence of AD. It would enable persons with high level SCIs to be more independent and pursue vocational activities while granting continuous medical oversight. Clinical Impact: The PTS could serve as a supplementary tool to current solutions to detect the onset of AD and prepare individuals who are newly injured to be better prepared for AD episodes. Moreover, it could be translated into a system to encourage individuals to practice better healthcare management to prevent future occurrences.

中文翻译：

---

通过多模态传感自动检测症状性自主神经反射异常

目的：自主神经反射异常 (AD) 是一种潜在危及生命的综合征，发生于脊髓损伤程度较高 (SCI) 的个体中。AD 是由触发因素引起的，触发因素可导致病理生理反应迅速升级，如果不消除触发因素，AD 可能致命。目前还没有客观、非侵入性且准确的监测系统可以在非临床环境中实时自动检测 AD 症状的发作。技术或方法：我们利用 11 名 SCI 参与者的数据，通过可穿戴生理遥测系统 (PTS) 和机器学习模型开发了一种独立于用户的 AD 症状实时检测方法。结果：PTS能够检测AD症状的发作，平均准确率为94.10%，假阴性率为4.89%。结论：PTS能够以较高的敏感性和特异性检测AD症状的出现，以帮助SCI患者预防AD的发生。它将使具有高水平 SCI 的人更加独立并从事职业活动，同时给予持续的医疗监督。临床影响：PTS 可以作为当前解决方案的补充工具，用于检测 AD 的发作，并让新受伤的个体为 AD 发作做好更好的准备。此外，它可以转化为一个系统，鼓励个人实行更好的医疗管理，以防止未来发生类似情况。