使用人工神经网络进行空气栓塞检测与分级的初步实验室研究。

A preliminary laboratory investigation of air embolus detection and grading using an artificial neural network.

作者信息

Strong K, Westenskow D R, Fine P G, Orr J A

机构信息

Department of Bioengineering, University of Utah Health Science Center, Salt Lake City 84132, USA.

出版信息

Int J Clin Monit Comput. 1997;14(2):103-7. doi: 10.1007/BF03356584.

DOI:10.1007/BF03356584

PMID:9336735

Abstract

SUMMARY STATEMENT

Processed digitized Doppler signals abstracted from recordings during continuous air infusion in dogs were used to train a neural network to estimate air embolism infusion rates.

BACKGROUND

Precordial Doppler is a sensitive technique for detecting venous air embolism during anesthesia, but it requires constant attentive listening. Since neural networks are particularly well suited to the task of pattern recognition, we sought to investigate this technology for detection and grading of air embolism.

METHODS

Air was infused into peripheral veins of four anesthetized dogs at rates of 0.025, 0.05, 0.10, 0.25, 0.50 and 1.0 ml-1.kg-1.min-1 while digital recordings of the precordial Doppler ultrasound signal were collected. The frequency content of the recordings was determined by Fourier analysis. The output of the Fourier transform was the input to a neural network. The network was then trained to estimate the air infusion rate.

RESULTS

The correlation coefficient between the size of the air embolism and the air infusion rate was greater than r2 = 0.93 for each of the four animals in the study when the network was trained using the data for all four dogs. When the data from a dog was withheld from the training set and used only for testing the correlation coefficients ranged from r2 = 0.75 to r2 = 0.27. For frequencies below 250 Hz, the acoustic energy tended to fall as the air infusion rate increased. The opposite occurred at frequencies above 325 Hz.

CONCLUSIONS

Neural network processing of the precordial Doppler signal provides a quantitative estimate of the size of an air embolism.

摘要

摘要声明

从犬类连续空气输注期间的记录中提取的经处理的数字化多普勒信号用于训练神经网络以估计空气栓塞输注速率。

背景

心前区多普勒是一种在麻醉期间检测静脉空气栓塞的敏感技术，但它需要持续专注地聆听。由于神经网络特别适合模式识别任务，我们试图研究该技术用于空气栓塞的检测和分级。

方法

以0.025、0.05、0.10、0.25、0.50和1.0 ml⁻¹·kg⁻¹·min⁻¹的速率将空气注入四只麻醉犬的外周静脉，同时收集心前区多普勒超声信号的数字记录。通过傅里叶分析确定记录的频率成分。傅里叶变换的输出作为神经网络的输入。然后训练该网络以估计空气输注速率。

结果

当使用所有四只犬的数据训练网络时，研究中的四只动物每只的空气栓塞大小与空气输注速率之间的相关系数均大于r² = 0.93。当将一只犬的数据从训练集中 withheld 并仅用于测试时，相关系数范围为r² = 0.75至r² = 0.27。对于低于250 Hz的频率，随着空气输注速率增加，声能趋于下降。在高于325 Hz的频率下则出现相反情况。