广东医学2024，Vol.45Issue(9)：1106-1112,7.DOI:10.13820/j.cnki.gdyx.20241851
应用平均幅度差函数之和分析心电信号对除颤最佳时机的预测价值
Predictive value of the sum of average magnitude difference function in determining optimal defibrillation timing by analyzing ECG signals
摘要
Abstract
Objective To evaluate the efficacy of the sum of average magnitude difference function(SAMDF)in processing ventricular fibrillation(VF)ECG signals for predicting optimal defibrillation timing,and to compare it with the commonly used amplitude spectrum area(AMSA)method.Methods Fifty-six male pigs,each weighing 40±5 kg,were used for the study.VF was induced,followed by 10 minutes of untreated VF,6 minutes of cardiopulmonary resusci-tation(CPR),and subsequent defibrillation.SAMDF and AMSA values were recorded every minute during VF and CPR.Receiver operating characteristic(ROC)curves were calculated,and one-way analyses of variance(one-way ANOVA)and positive-negative sample scatter plots were used to compare the two methods for optimizing defibrillation timing pre-diction.The SAMDF and AMSA values of the successful defibrillation group(Group R)and the unsuccessful defibrillation group(Group N)were compared to evaluate their predictive abilities.Results Scatter plots demonstrated that both SAM-DF and AMSA could differentiate between positive and negative samples(P＜0.001).ROC analysis showed that SAMDF(AUC=0.801,P＜0.001)and AMSA(AUC=0.777,P＜0.001)had similar capabilities in predicting optimal defib-rillation timing.The SAMDF and AMSA values were significantly higher in Group R than in Group N(P＜0.001).Conclusion SAMDF shows strong potential for optimizing the prediction of defibrillation timing and could serve as a com-plement to existing effective predictors like AMSA.关键词
平均幅度差函数之和/振幅谱面积/心电信号/预测除颤时机Key words
the sum of average magnitude difference function/amplitude spectrum area/ECG signal/predicting defibrillation timing分类
医药卫生引用本文复制引用
刘远山,林帆荣,陈煜嘉,黄子通,蒋龙元,杨正飞..应用平均幅度差函数之和分析心电信号对除颤最佳时机的预测价值[J].广东医学,2024,45(9):1106-1112,7.基金项目
广东省基础与应用基础研究基金项目-区域联合基金-地区培育项目(2022A1515140033) （2022A1515140033）
广州市科技计划项目(2023A04J2094,2023A03J0714) （2023A04J2094,2023A03J0714）
