Caminal P, Vallverdú M, Giraldo B, Benito S, Vázquez G, Voss A
ESAII Department, Catalonian Bioengineering Research Centre (CREBEC), Technical University of Catalonia (UPC), Barcelona, Spain.
IEEE Trans Biomed Eng. 2005 Nov;52(11):1832-9. doi: 10.1109/TBME.2005.856293.
Traditional time domain techniques of data analysis are often not sufficient to characterize the complex dynamics of respiration. In this paper, the respiratory pattern variability is analyzed using symbolic dynamics. A group of 20 patients on weaning trials from mechanical ventilation are studied at two different pressure support ventilation levels, in order to obtain respiratory volume signals with different variability. Time series of inspiratory time, expiratory time, breathing duration, fractional inspiratory time, tidal volume and mean inspiratory flow are analyzed. Two different symbol alphabets, with three and four symbols, are considered to characterize the respiratory pattern variability. Assessment of the method is made using the 40 respiratory volume signals classified using clinical criteria into two classes: low variability (LV) or high variability (HV). A discriminant analysis using single indexes from symbolic dynamics has been able to classify the respiratory volume signals with an out-of-sample accuracy of 100%.
传统的时域数据分析技术往往不足以表征呼吸的复杂动态。本文利用符号动力学分析呼吸模式变异性。对一组20名正在进行机械通气撤机试验的患者在两种不同的压力支持通气水平下进行研究，以获得具有不同变异性的呼吸容积信号。分析吸气时间、呼气时间、呼吸持续时间、吸气分数时间、潮气量和平均吸气流量的时间序列。考虑使用具有三个和四个符号的两种不同符号字母表来表征呼吸模式变异性。使用根据临床标准分为低变异性（LV）或高变异性（HV）两类的40个呼吸容积信号对该方法进行评估。使用来自符号动力学的单一指标进行的判别分析能够以100%的样本外准确率对呼吸容积信号进行分类。
