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首页> 外文会议>13th Conference on Integrated Optics: Sensors, Sensing Structures, and Methods >Investigating the Possibility of Using a Neural Network to Determine the Stroke Volume of a New Pneumatic Heart Prosthesis Model
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Investigating the Possibility of Using a Neural Network to Determine the Stroke Volume of a New Pneumatic Heart Prosthesis Model

机译：研究使用神经网络确定新型气动心脏假体模型的卒中量的可能性

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The work concerns the study of the possibility of using an artificial neural network to determine the ejection volume of pulsatile models of heart assist pumps. The research used new pump designs, significantly different from those used in terms of dimensions and the material from which the flaccid membrane was made. The basis for determining the ejection volume are the special features of the membrane view, which is obtained from the vision sensor. The essence of the method operation depends on associating the membrane view with the corresponding reference volume value, which during the network learning process, is read from the burette with an accuracy of ±0.5 ml. The operation of the artificial neural network consists in the identification of artifacts on the examined views of the membranes and associating them with the ejection volume values. In the case where the membrane view cannot be univocally qualified to the training set, the network acts as an interpolator and predicts the stroke volume value. Verifying the ability to determine the stroke volume by the neural network was performed in close-to-real conditions. In addition to the test results, the article presents new pump designs, the laboratory station and the course of the experiment.
机译：这项工作涉及使用人工神经网络确定心脏辅助泵搏动模型的射血体积的可能性的研究。这项研究使用了新的泵设计，在尺寸和制造松弛膜的材料方面与所使用的泵明显不同。确定喷射量的基础是从视觉传感器获得的膜片视图的特殊功能。方法操作的本质取决于将膜视图与相应的参考体积值相关联，在网络学习过程中，该参考体积值是从滴定管中读取的，精度为±0.5 ml。人工神经网络的操作在于识别膜的检查视图上的伪影，并将其与喷射量值相关联。在无法完全使膜视图适合训练集的情况下，网络充当插值器并预测笔划量值。在接近真实的条件下，通过神经网络验证了确定冲程量的能力。除了测试结果外，本文还介绍了新的泵设计，实验室工作站和实验过程。

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