心率变异性分析在新生儿疼痛检测中的应用

为了研究疼痛暴露对新生儿自主神经系统的影响,并建立基于心率变异性(heart rate variability,HRV)指标的新生儿疼痛检测模型,采用时域、频域和非线性方法对40名新生儿疼痛暴露前后的心电数据进行短时HRV分析,Wilcoxon符号秩检验用于统计分析,支持向量机(support vector machine,SVM)用于建立检测模型.结果表明,RR间期均值a RR、低频段功率LF、高频段功率HF等3个线性指标和近似熵Ap En、样本熵Samp En、递归率REC等9个非线性指标在疼痛前后具有统计学差异;基于a RR、相邻两个RR间期对差值大于50 ms的百分比p NN50,Ap En,关联维D2和REC等5个指标和SVM的疼痛检测模型检测正确率达到83.75%.HRV的相关指标可反映新生儿自主神经系统对疼痛暴露的应答,基于HRV指标和SVM的模型可用于新生儿疼痛检测.

Application of heart rate variability analysis to pain detection for newborns

To investigate the influence of pain exposure on autonomic nervous system of newborns, and develop a detection model based on heart rate variability (HRV) indexes, 40 newborns are recruited in the study and short-term HRV analyses are performed on electrocardiogram before and after pain exposure using time-domain, frequency domain and nonlinear methods. Wilcoxon signed rank test is adopted for statistical comparison, and the support vector machine (SVM) is used for developing a detection model. The results demonstrate that 3 linear indexes such as the mean of RR intervals aRR, absolute powers of low frequency band LF and absolute powers of high frequency band HF, and 9 nonlinear indexes such as approximate entropy ApEn, sample entropy SampEn, and determinism DET before pain exposure are significantly different from after pain exposure; and that a detection accuracy of 83.75% could be achieved by the model based on the combination of 5 indexes, i.e., aRR, proportion of adjacent intervals greater than 50 ms pNN50, ApEn, correlation dimension D₂ and recurrence rate REC, and SVM. It suggests that HRV indexes can reveal the response of autonomous nervous system to pain exposure of newborns, and the model based on HRV indexes and SVM could be employed for the detection of pain.
Keywords： heart rate variability newborns pain support vector machine