Alphonse Sebastian, Polichnowski Aaron J, Griffin Karen A, Bidani Anil K, Williamson Geoffrey A
Dept. of Elec. and Comp. Engr., Illinois Institute of Technology Chicago, IL, U.S.A.
Department of Biomedical Sciences East Tennessee State University, Johnson City, TN, U.S.A.
Proc Eur Signal Process Conf EUSIPCO. 2020;2020:1165-1169. doi: 10.23919/eusipco47968.2020.9287447. Epub 2020 Dec 18.
A convolutional deep neural network is employed to assess renal autoregulation using time series of arterial blood pressure and blood flow rate measurements in conscious rats. The network is trained using representative data samples from rats with intact autoregulation and rats whose autoregulation is impaired by the calcium channel blocker amlodipine. Network performance is evaluated using test data of the types used for training, but also with data from other models for autoregulatory impairment, including different calcium channel blockers and also renal mass reduction. The network is shown to provide effective classification for impairments from calcium channel blockers. However, the assessment of autoregulation when impaired by renal mass reduction was not as clear, evidencing a different signature in the hemodynamic data for that impairment model. When calcium channel blockers were given to those animals, however, the classification again was effective.
使用卷积深度神经网络，通过清醒大鼠动脉血压和血流速率测量的时间序列来评估肾自动调节功能。该网络使用来自具有完整自动调节功能的大鼠以及自动调节功能因钙通道阻滞剂氨氯地平而受损的大鼠的代表性数据样本进行训练。网络性能使用与训练所用类型相同的测试数据进行评估，同时也使用来自其他自动调节功能受损模型的数据进行评估，包括不同的钙通道阻滞剂以及肾质量减少模型。结果表明，该网络能够有效地对钙通道阻滞剂导致的损伤进行分类。然而，对于肾质量减少导致的自动调节功能损伤的评估并不那么明确，这表明该损伤模型的血流动力学数据具有不同的特征。然而，当给这些动物使用钙通道阻滞剂时，分类再次有效。
