Yavuz Tahsin T, Claassen Jan, Kleinberg Samantha
Stevens Institute of Technology, Hoboken, NJ.
Columbia University, New York, NY.
AMIA Annu Symp Proc. 2020 Mar 4;2019:942-951. eCollection 2019.
Consciousness is a highly significant indicator of an ICU patient's condition but there is still no method to automatically measure it. Instead, time consuming and subjective assessments are used. However, many brain and physiologic variables are measured continuously in neurological ICU, and could be used as indicators for consciousness. Since many biological variables are highly correlated to maintain homeostasis, we examine whether changes in time lags between correlated variables may relate to changes in consciousness. We introduce new methods to identify changes in the time lag of correlations, which better handle noisy multimodal physiological data and fluctuating lags. On neurological ICU data from subarachnoid hemorrhage patients, we find that correlations among variables related to brain physiology or respiration have significantly longer lags inpatients with decreased levels of consciousness than in patients with higher levels of consciousness. This suggests that physiological data could potentially be used to automatically assess consciousness.
意识是重症监护病房(ICU)患者病情的一个非常重要的指标,但目前仍没有自动测量它的方法。相反,采用的是耗时且主观的评估方法。然而,在神经重症监护病房中,许多脑和生理变量是持续测量的,并且可以用作意识的指标。由于许多生物变量高度相关以维持体内平衡,我们研究相关变量之间时间滞后的变化是否可能与意识的变化有关。我们引入了新的方法来识别相关性时间滞后的变化,这些方法能更好地处理有噪声的多模态生理数据和波动的滞后。在来自蛛网膜下腔出血患者的神经重症监护病房数据中,我们发现与脑生理或呼吸相关的变量之间的相关性在意识水平降低的患者中比意识水平较高的患者具有明显更长的滞后。这表明生理数据有可能用于自动评估意识。