Hoeksel S A, Jansen J R, Blom J A, Schreuder J J
Department of Anesthesiology, Cardiovascular Research Institute Maastricht, Maastricht University, The Netherlands.
J Clin Monit. 1997 Sep;13(5):309-16. doi: 10.1023/a:1007414906294.
A novel algorithm to detect the dicrotic notch in arterial pressure signals is proposed. Its performance is evaluated using both aortic and radial artery pressure signals, and its robustness to variations in design parameters is investigated.
Most previously published dicrotic notch detection algorithms scan the arterial pressure waveform for the characteristic pressure change that is associated with the dicrotic notch. Aortic valves, however, are closed by the backwards motion of aortic blood volume. We developed an algorithm that uses arterial flow to detect the dicrotic notch in arterial pressure waveforms. Arterial flow is calculated from arterial pressure using simulation results with a three-element windkessel model. Aortic valve closure is detected after the systolic upstroke and at the minimum of the first negative dip in the calculated flow signal.
In 7 dogs ejection times were derived from a calculated aortic flow signal and from simultaneously measured aortic flow probe data. A total of 86 beats was analyzed; the difference in ejection times was -0.6 +/- 5.4 ms (means +/- SD). The algorithm was further evaluated using 6 second epochs of radial artery pressure data measured in 50 patients. Model simulations were carried out using both a linear windkessel model and a pressure and age dependent nonlinear windkessel model. Visual inspection by an experienced clinician confirmed that the algorithm correctly identified the dicrotic notch in 98% (49 of 50) of the patients using the linear model, and 96% (48 of 50) of the patients using the nonlinear model. The position of the dicrotic notch appeared to be less sensitive to variations in algorithm's design parameters when a nonlinear windkessel model was used.
The detection of the dicrotic notch in arterial pressure signals is facilitated by first calculating the arterial flow waveform from arterial pressure and a model of arterial afterload. The method is robust and reduces the problem of detecting a dubious point in a decreasing pressure signal to the detection of a well-defined minimum in a derived signal.
提出一种用于检测动脉压力信号中重搏波切迹的新算法。使用主动脉和桡动脉压力信号对其性能进行评估，并研究其对设计参数变化的鲁棒性。
大多数先前发表的重搏波切迹检测算法会扫描动脉压力波形以寻找与重搏波切迹相关的特征性压力变化。然而，主动脉瓣是由主动脉血容量的反向运动关闭的。我们开发了一种利用动脉血流来检测动脉压力波形中重搏波切迹的算法。使用三元风箱模型的模拟结果从动脉压力计算动脉血流。在收缩期上升之后且在计算出的血流信号中第一个负向波谷的最低点处检测到主动脉瓣关闭。
在7只犬中，从计算出的主动脉血流信号和同时测量的主动脉血流探头数据得出射血时间。总共分析了86次搏动；射血时间的差异为-0.6±5.4毫秒（均值±标准差）。使用在50名患者中测量的6秒时长的桡动脉压力数据进一步评估该算法。使用线性风箱模型和压力及年龄相关的非线性风箱模型进行了模型模拟。一位经验丰富的临床医生通过目视检查确认，使用线性模型时，该算法在98%（50例中的49例）的患者中正确识别出重搏波切迹，使用非线性模型时，在96%（50例中的48例）的患者中正确识别出重搏波切迹。当使用非线性风箱模型时，重搏波切迹的位置似乎对算法设计参数的变化不太敏感。
通过首先根据动脉压力和动脉后负荷模型计算动脉血流波形，有助于检测动脉压力信号中的重搏波切迹。该方法具有鲁棒性，并将在下降的压力信号中检测可疑点的问题简化为在导出信号中检测明确的最低点的问题。
