Cysarz Dirk, Edelhauser Friedrich, Javorka Michal, Montano Nicola, Porta Alberto
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:286-9. doi: 10.1109/EMBC.2015.7318356.
Coarse graining of physiological time series such as the cardiac interbeat interval series by means of a symbolic transformation retains information about dynamical properties of the underlying system and complements standard measures of heart rate variability. The transformations of the original time series to the coarse grained symbolic series usually lead to a non-uniform occurrence of the different symbols, i.e. some symbols appear more often than others influencing the results of the subsequent symbolic series analysis. Here, we defined a transformation procedure to assure that each symbol appears with equal probability using a short alphabet {0,1,2,3} and a long alphabet {0,1,2,3,4,5}. The procedure was applied to the cardiac interbeat interval series RRi of 17 healthy subjects obtained during graded head-up tilt testing. The symbolic dynamics is analyzed by means of the occurrence of short sequences (`words') of length 3. The occurrence of words is grouped according to words without variations of the symbols (0V%), one variation (1V%), two like variations (2LV%) and two unlike variations (2UV%). Linear regression analysis with respect to tilt angle showed that for the short alphabet 0V% increased with increasing tilt angle whereas 1V%, 2LV% and 2UV% decreased. For the long alphabet 0V%, and 1V% increased with increasing tilt angle whereas 2LV% and 2UV% decreased. These results were slightly better compared to the results from non-uniform symbolic transformations reflecting the deviation from the mean. In conclusion, the symbolic transformation assuring the appearance of symbols with equal probability is capable of reflecting changes of the cardiac autonomic nervous system during graded head-up tilt. Furthermore, the transformation is independent of the time series' distribution.
通过符号变换对诸如心脏搏动间期序列等生理时间序列进行粗粒化，能够保留关于基础系统动力学特性的信息，并补充心率变异性的标准测量方法。将原始时间序列转换为粗粒化符号序列通常会导致不同符号出现的频率不均匀，即某些符号比其他符号出现得更频繁，这会影响后续符号序列分析的结果。在此，我们定义了一种变换程序，使用短字母表{0,1,2,3}和长字母表{0,1,2,3,4,5}来确保每个符号以相等的概率出现。该程序应用于17名健康受试者在分级头高位倾斜试验期间获得的心脏搏动间期序列RRi。通过长度为3的短序列（“单词”）的出现情况来分析符号动力学。单词的出现情况根据符号无变化的单词（0V%）、一种变化（1V%）、两种相同变化（2LV%）和两种不同变化（2UV%）进行分组。关于倾斜角度的线性回归分析表明，对于短字母表，0V%随倾斜角度增加而增加，而1V%、2LV%和2UV%则下降。对于长字母表，0V%和1V%随倾斜角度增加而增加，而2LV%和2UV%下降。与反映偏离均值的非均匀符号变换结果相比，这些结果略好。总之，确保符号以相等概率出现的符号变换能够反映分级头高位倾斜期间心脏自主神经系统的变化。此外，该变换与时间序列的分布无关。
