Raphan Theodore, Yakushin Sergei B
Department of Computer and Information Science, Institute for Neural and Intelligent Systems, Brooklyn College of CUNY, Brooklyn, NY, United States.
Graduate Center of CUNY, New York, NY, United States.
Front Neurol. 2021 Mar 10;12:631409. doi: 10.3389/fneur.2021.631409. eCollection 2021.
Vasovagal syncope () or neurogenically induced fainting has resulted in falls, fractures, and death. Methods to deal with are to use implanted pacemakers or beta blockers. These are often ineffective because the underlying changes in the cardiovascular system that lead to the syncope are incompletely understood and diagnosis of frequent occurrences of is still based on history and a tilt test, in which subjects are passively tilted from a supine position to 20° from the spatial vertical (to a 70° position) on the tilt table and maintained in that orientation for 10-15 min. Recently, is has been shown that vasovagal responses (), which are characterized by transient drops in blood pressure (), heart rate (), and increased amplitude of low frequency oscillations in can be induced by sinusoidal galvanic vestibular stimulation (sGVS) and were similar to the low frequency oscillations that presaged in humans. This transient drop in and of 25 mmHg and 25 beats per minute (bpm), respectively, were considered to be a . Similar thresholds have been used to identify in human studies as well. However, this arbitrary threshold of identifying a does not give a clear understanding of the identifying features of a VVR nor what triggers a . In this study, we utilized our model of generation together with a machine learning approach to learn a separating hyperplane between normal and patterns. This methodology is proposed as a technique for more broadly identifying the features that trigger a . If a similar feature identification could be associated with in humans, it potentially could be utilized to identify onset of a , i.e, fainting, in real time.
血管迷走性晕厥（VVS）或神经源性晕厥可导致跌倒、骨折甚至死亡。应对VVS的方法包括使用植入式起搏器或β受体阻滞剂。但这些方法往往效果不佳，因为导致晕厥的心血管系统潜在变化尚未完全明确，且VVS频发的诊断仍基于病史和倾斜试验，即让受试者在倾斜台上从仰卧位被动倾斜至与空间垂直方向呈20°（即70°位置），并保持该姿势10 - 15分钟。最近有研究表明，血管迷走反应（VVRs）的特征是血压（BP）、心率（HR）短暂下降，以及低频振荡幅度增加，而正弦电前庭刺激（sGVS）可诱发VVRs，且这些反应与人类晕厥先兆的低频振荡相似。收缩压和心率分别下降25 mmHg和25次/分钟（bpm）的这种短暂下降被视为一次VVR。在人体研究中也使用了类似的阈值来识别VVR。然而，这种识别VVR的任意阈值并不能清晰地揭示VVR的识别特征，也无法明确引发VVR的因素。在本研究中，我们利用我们的VVS产生模型以及机器学习方法来学习正常模式和VVS模式之间的分离超平面。该方法被提议作为一种更广泛地识别引发VVS特征的技术。如果在人类中能将类似的特征识别与VVS相关联，那么它有可能被用于实时识别VVS的发作，即晕厥。
