Estimation of pulmonary artery occlusion pressure by an artificial neural network
Estimation of pulmonary artery occlusion pressure by an artificial neural network
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通过人工神经网络估计肺动脉闭塞压
DOI：
10.1097/00003246-200301000-00041
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发表时间：
2003
影响因子：
8.8
通讯作者：
M. Levitzky
中科院分区：
文献类型：
--
作者：
B. deBoisblanc;A. Pellett;Royce W. Johnson;M. Champagne;Espisito McClarty;G. Dhillon;M. Levitzky
目的建立具有自适应和学习能力的人工神经网络，从脉搏肺动脉波形中准确估计肺动脉闭塞压力。学校医疗中心。19只闭胸犬。在常规肺动脉闭塞压测量前，在对照条件下，在输注血清素或组胺期间，或在容量负荷期间，对肺动脉波形进行数字化采样。单个节拍被解析或分离出来。肺动脉压、其一阶导数和心跳持续时间作为神经输入。神经网络使用所有样本的80%进行训练，并对剩余的20%进行测试。为了比较，使用相同的数据集开发并测试了肺动脉舒张压和肺动脉闭塞压之间的回归。作为泛化性的最后测试，神经网络在18只狗的数据上进行训练，并以循环方式对其余狗的数据进行测试。测量结果与主要结果肺动脉舒张压与肺动脉闭塞压测量值的相关系数为。75，神经网络估计肺动脉闭塞压为。肺动脉舒张压与肺动脉闭塞压的差异p < 0.01)。肺动脉舒张压对肺动脉闭塞压的估计偏差为0.097 mm Hg（一致性界限为- 7.57至7.767 mm Hg），而神经网络对肺动脉闭塞压的估计偏差为- 0.002 mm Hg（- 2.592至2.588 mm Hg）。在测试值的范围内，神经网络估计的偏差没有显著变化。相反，肺动脉舒张压的估计偏差随着肺动脉闭塞压的增加而显著增加。在循环测试中，神经网络对肺动脉闭塞压的估计表现不佳（估计肺动脉闭塞压与测量肺动脉闭塞压的相关系数为0.59）。结论在肺动脉血流动力学改变的条件下，神经网络可以在大范围内准确估计肺动脉闭塞压。我们推测，人工神经网络可以为危重患者提供准确、实时的肺动脉闭塞压估计。
ObjectiveWe hypothesized that an artificial neural network, interconnected computer elements capable of adaptation and learning, could accurately estimate pulmonary artery occlusion pressure from the pulsatile pulmonary artery waveform. SettingUniversity medical center. SubjectsNineteen closed-chest dogs. InterventionsPulmonary artery waveforms were digitally sampled before conventional measurements of pulmonary artery occlusion pressure under control conditions, during infusions of serotonin or histamine, or during volume loading. Individual beats were parsed or separated out. Pulmonary artery pressure, its first time derivative, and the beat duration were used as neural inputs. The neural network was trained by using 80% of all samples and tested on the remaining 20%. For comparison, the regression between pulmonary artery diastolic pressure and pulmonary artery occlusion pressure was developed and tested using the same data sets. As a final test of generalizability, the neural network was trained on data obtained from 18 dogs and tested on data from the remaining dog in a round-robin fashion. Measurements and Main ResultsThe correlation coefficient between the pulmonary artery diastolic pressure estimate of pulmonary artery occlusion pressure and measured pulmonary artery occlusion pressure was .75, whereas that for the neural network estimate of pulmonary artery occlusion pressure was .97 (p < .01 for difference between pulmonary artery diastolic pressure and pulmonary artery occlusion pressure estimates). The pulmonary artery diastolic pressure estimate of pulmonary artery occlusion pressure showed a bias of 0.097 mm Hg (limits of agreement −7.57 to 7.767 mm Hg), whereas the neural network estimate of pulmonary artery occlusion pressure showed a bias of −0.002 mm Hg (−2.592 to 2.588 mm Hg). There was no significant change in the bias of the neural network estimate over the range of values tested. In contrast, the bias for the pulmonary artery diastolic pressure estimate significantly increased with the increasing magnitude of the pulmonary artery occlusion pressure. During round-robin testing, the neural network estimate of pulmonary artery occlusion pressure showed suboptimal performance (correlation coefficient between estimated and measured pulmonary artery occlusion pressure .59). ConclusionsA neural network can accurately estimate pulmonary artery occlusion pressure over a wide range of pulmonary artery occlusion pressure under conditions that alter pulmonary hemodynamics. We speculate that artificial neural networks could provide accurate, real-time estimates of pulmonary artery occlusion pressure in critically ill patients.