一种基于马尔可夫模型的早期呼吸窘迫检测方法。

An early respiratory distress detection method with Markov models.

作者信息

Ravishankar Hariharan, Saha Aditya, Swamy Gokul, Genc Sahika

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:3438-41. doi: 10.1109/EMBC.2014.6944362.

DOI:10.1109/EMBC.2014.6944362

Abstract

A method for early detection of respiratory distress in hospitalized patients which is based on a multi-parametric analysis of respiration rate (RR) and pulse oximetry (SpO2) data trends to ascertain patterns of patient instability pertaining to respiratory distress is described. Current practices of triggering caregiver alerts are based on simple numeric threshold breaches of SpO2. The pathophysiological patterns of respiratory distress leading to in-hospital deaths are much more complex to be detected by numeric thresholds. Our pattern detection algorithm is based on a Markov model framework based on multi-parameter pathophysiological patterns of respiratory distress, and triggers in a timely manner and prior to the violation of SpO2 85-90% threshold, providing additional lead time to attempt to reverse the deteriorating state of the patient. We present the performance of the algorithm on MIMIC II dataset resulting in true positive rate of 92% and false positive rate of 6%.

摘要

描述了一种用于早期检测住院患者呼吸窘迫的方法，该方法基于对呼吸频率（RR）和脉搏血氧饱和度（SpO2）数据趋势的多参数分析，以确定与呼吸窘迫相关的患者不稳定模式。当前触发护理人员警报的做法基于SpO2的简单数值阈值突破。导致住院死亡的呼吸窘迫的病理生理模式要通过数值阈值来检测则更为复杂。我们的模式检测算法基于基于呼吸窘迫多参数病理生理模式的马尔可夫模型框架，并在违反SpO2 85 - 90%阈值之前及时触发，为尝试扭转患者病情恶化状态提供额外的提前时间。我们展示了该算法在MIMIC II数据集上的性能，其真阳性率为92%，假阳性率为6%。