Department of Cardiovascular Medicine, Zhongshan People's Hospital, Zhongshan 528400, China.
Comput Math Methods Med. 2022 Mar 9;2022:4596552. doi: 10.1155/2022/4596552. eCollection 2022.
The objective of this study was to explore the predictive value of electrocardiogram (ECG) based on intelligent analysis algorithm for atrial fibrillation (AF) in elderly patients undergoing coronary artery bypass grafting (CABG). Specifically, 106 elderly patients with coronary heart disease who underwent CABG in the hospital were selected, including 52 patients with postoperative AF (AF group) and 54 patients without arrhythmia (control group). Within 1-3 weeks after operation, the dynamic ECG monitoring system based on Gentle AdaBoost algorithm constructed in this study was adopted. After the measurement of the 12-lead P wave duration, the maximum P wave duration (Pmax) and minimum P wave duration (Pmin) were recorded. As for simulation experiments, the same data was used as the back-propagation algorithm. The results showed that for the detection accuracy of the test samples, the Gentle AdaBoost algorithm showed 93.7% accuracy after the first iteration, and the Gentle AdaBoost algorithm was 16.1% higher than the back-propagation algorithm. Compared with the control group, the detection rate of arrhythmia in patients after CABG was significantly lower ( < 0.05). Bivariate logistic regression analysis on Pmax and Pmin showed as follows: Pmax: 95% confidential interval (CI): 1.024-1.081, < 0.05; Pmin: 95% CI: 1.036-1.117, < 0.05. The sensitivity of Pmax and Pmin in predicting paroxysmal AF was 78.2% and 73.4%, respectively; the specificity of them was 80.1% and 85.6%, respectively; the positive predictive value was 81.2% and 83.4%, respectively; and the negative predictive value was 79.5% and 75.3%, respectively. In conclusion, the generalization ability of Gentle AdaBoost algorithm was better than that of back-propagation algorithm, and it can identify arrhythmia better. Pmax and Pmin were important indicators of AF after CABG.
本研究旨在探讨基于智能分析算法的心电图（ECG）对老年冠状动脉旁路移植术（CABG）患者心房颤动（AF）的预测价值。具体地，选择了医院 106 例接受 CABG 的老年冠心病患者，其中术后发生房颤（AF 组）52 例，无心律失常（对照组）54 例。术后 1-3 周，采用本研究构建的基于 Gentle AdaBoost 算法的动态心电图监测系统。测量 12 导联 P 波时限后，记录最大 P 波时限（Pmax）和最小 P 波时限（Pmin）。对于模拟实验，同样的数据被用作反向传播算法。结果表明，对于测试样本的检测精度，Gentle AdaBoost 算法在第一次迭代后达到 93.7%的准确率，比反向传播算法高 16.1%。与对照组相比，CABG 后患者的心律失常检出率明显降低（<0.05）。对 Pmax 和 Pmin 的二元逻辑回归分析显示：Pmax：95%置信区间（CI）：1.024-1.081，<0.05；Pmin：95%CI：1.036-1.117，<0.05。Pmax 和 Pmin 预测阵发性 AF 的敏感性分别为 78.2%和 73.4%，特异性分别为 80.1%和 85.6%，阳性预测值分别为 81.2%和 83.4%，阴性预测值分别为 79.5%和 75.3%。总之，Gentle AdaBoost 算法的泛化能力优于反向传播算法，能更好地识别心律失常。Pmax 和 Pmin 是 CABG 后 AF 的重要指标。
