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使用深度学习对脏的自我调节效率评估

Sebastian Alphonse¹, Aaron J Polichnowski², Karen A Griffin³

¹Dept. of Elec. and Comp. Engr., Illinois Institute of Technology Chicago, IL, U.S.A.

Proceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference)

|January 30, 2024

概括

一个深度神经网络有效地通过使用血压和流量数据在老鼠中评估自身调节障碍. 它准确地识别了由通道阻塞剂引起的损伤,但在质量减少时显示的清晰度较低.

科学领域:

生理学生理学生理学
生物医学工程生物医学工程
机器学习机器学习

背景情况:

自身调节对于维持稳定的功能至关重要.
评估自我调节障碍对于了解病至关重要.
目前评估自身调节的方法可能是复杂的和侵入性的.

研究的目的:

开发和评估一个卷积深度神经网络来评估自身调节.
确定网络能够分类不同类型的自我调节障碍的能力.
通过各种药物和手术模型的损伤来研究网络的性能.

主要方法:

利用了意识大鼠的动脉血压和血流率的时间序列数据.
训练了一个卷积深度神经网络,使用完整和受损自我调节的老鼠的数据.
使用不同损伤模型的数据测试了网络,包括通道阻断剂和质量减少.

主要成果:

该网络实现了由通道阻断剂诱导的自我调节障碍的有效分类.
由于质量减少而导致的自我调节障碍的评估不太清楚,表明明显的血液动力学特征.
结合性功能障碍 (质减少加通道阻断剂) 得到了有效的分类.

结论:

卷积深度神经网络显示出对自身调节的非侵入性评估的前景.
网络的表现因具体的自我调节损害模型而异.
可能需要进一步细化,以准确评估由结构变化如质量减少造成的损伤.

关键词:

生物医学信号处理机器学习是机器学习.腎臟病學 (nephrology) 是一種醫學.神经网络的神经网络的神经网络生理学生理学身体学

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