■光谱通量(SF),它基于音频处理领域的常见算法,应用于ECG信号的定量分析,以优化除颤的时机。为了证明在优化除颤定时方面的性能,在回顾性分析实验中,将SF与猪心室纤颤(VF)模型中的振幅谱面积(AMSA)进行了比较。
■共有56头雄性家猪,体重40±5公斤,被诱导接受VF。然后将动物不治疗10分钟,并在心肺复苏(CPR)6分钟后进行除颤。在VF和CPR期间每分钟计算各自的SF和AMSA值。通过受试者工作特征(ROC)曲线进行比较,单向方差分析(单向方差分析),和成功的初始除颤样本的散点图(阳性样本,R组)和失败的初始除颤样本(阴性样本,组N)来说明AMSA和SF方法在优化除颤定时方面的性能。
■SF和AMSA的值在10分钟VF期间逐渐降低,在6分钟CPR期间升高。散点图显示,两种指标均具有区分阳性和阴性样本的能力(p<.001)。同时,ROC曲线显示SF(曲线下面积,AUC=0.798,p<.001)与AMSA(AUC=0.737,p<.001)具有相同的能力来预测成功的除颤(Z=1.35,p=0.177)。此外,当比较成功的初始除颤样本(R组)和失败的初始除颤样本(N组)之间的AMSA和SF值时,结果显示,R组AMSA和SF值均显著高于N组(p<.001)。
■在本研究中,SF方法具有与AMSA相同的预测成功除颤的能力,在成功除颤的情况下的值明显高于除颤失败的情况。此外,由于频率范围较窄,SF方法可能比AMSA更稳定,可以滤除较高频率的干扰信号,并且比AMSA具有更高的特异性和预测准确性。因此,SF法具有较高的临床潜力,可以优化除颤时机。然而,在临床实践中,尚需进一步的动物和临床研究来证实SF作为除颤器预测模块的有效性和实用性.
UNASSIGNED: Spectral Flux (SF), which is based on common algorithms in the audio processing field, was applied to quantitatively analyze ECG signals to optimize the timing of defibrillation. With the aim of proving the performance in optimizing the timing of defibrillation, SF was compared with Amplitude Spectrum Area (AMSA) in a porcine model of ventricular fibrillation (VF) in a retrospective analysis experiment.
UNASSIGNED: A total of 56 male domestic pigs, weighing 40 ± 5 kg, were induced to undergo VF. Animals were then left untreated for 10 min, and after 6 min of cardiopulmonary resuscitation (CPR) defibrillation was performed. The respective SF and AMSA values were calculated every minute during VF and CPR. Comparisons were made through receiver operating characteristic (ROC) curves, one-way analyses of variance (one-way ANOVA), and scatterplots for the successful initial defibrillation sample (positive samples, Group R) and the failed initial defibrillation sample (negative samples, Group N) to illustrate the performance in optimizing the timing of defibrillation for the AMSA and SF methods.
UNASSIGNED: Values of SF and AMSA gradually decreased during the 10 min VF period and increased in during the 6 min CPR period. The scatterplots showed that both metrics had the ability to distinguish positive and negative samples (p < .001). Meanwhile, ROC curves showed that SF (area under the curve, AUC = 0.798, p < .001) had the same ability as AMSA (AUC = 0.737, p < .001) to predict the successful defibrillation (Z = 1.35, p = 0.177). Moreover, when comparing the values for AMSA and SF between the successful initial defibrillation samples (Group R) and the failed initial defibrillation samples (Group N), the results showed that the values of both AMSA and SF in Group R were significantly higher than those in Group N (p < .001).
UNASSIGNED: In the present study, SF method had the same ability as AMSA to predict successful defibrillation with significantly higher values in cases of successful defibrillation than the instances in which defibrillation failed. Additionally, SF method might be more stable than AMSA for filtering out the higher frequency interference signals due to the narrower frequency range and had higher specificity and predictive accuracy than AMSA. So SF method had high clinical potential to optimize the timing of defibrillation. Nevertheless, further animal and clinical studies are still needed to confirm the effectiveness and practicality of SF as a predictive module for defibrillators in clinical practice.