基于心率变异性(HRV)的特征分析，提出一种患者阵发性房颤(PAF)发作的预测系统方法。首先，基于一种新的自适应滤波技术逐次平滑滤波并粗粒化HRV后，采用熵量化HRV在多个自适应尺度的复杂性特征;其次，特征经Min-Max归一化和序列前向选择特征子集，输入支持向量机识别HRV类型，预测PAF发作。经50例时长5 min HRV序列集的五折交叉验证，得到最优预测结果为:准确率98%，敏感性100%，特异性96%，性能表现优越。另外，实验表明远离和紧随PAF时的HRV复杂性特征值在不同频率段内，分别具有不同的显著变化(P<0。05)，反映受试者神经系统调节心脏节律改变，以及调控机体、应激等适应外界环境变化能力的下降。
Prediction of paroxysmal atrial fibrillation based on heart rate variability analysis
Based on the analysis of heart rate variability(HRV),a prediction method for paroxysmal atrial fibrillation(PAF)attacks is proposed.A new adaptive filtering technique is used for smoothing and coarse graining of HRV,followed by entropy-based quantification of HRV complexity at multiple adaptive scales.After the features are normalized by Min-Max,feature subsets are selected by sequential forward selection method,and then input to support vector machine to identify HRV types and predict PAF attacks.Through 5-fold cross-validation on a set of 50 HRV sequences each lasting 5 minutes,the optimal prediction results are obtained:98%accuracy,100%sensitivity,96%specificity,demonstrating excellent performance.In addition,the experiment shows significant changes(P<0.05)in the complexity eigenvalues of HRV far away from and close to PAF at different frequency bands,reflecting alterations in nervous system regulation of cardiac rhythm and a decline in the ability to adapt to external environmental changes such as stress regulation.
paroxysmal atrial fibrillationheart rate variabilityentropyscaleintegral mean mode decomposition
万方数据
维普
