Hoyer D, Schmidt K, Zwiener U
Institut für Pathologische Physiologie, Klinikum der Friedrich-Schiller-Universität Jena.
Biomed Tech (Berl). 1995 Jul-Aug;40(7-8):190-4. doi: 10.1515/bmte.1995.40.7-8.190.
The aim of the present paper was, using artificial neural networks, to identify chaotic attractors presumed to be responsible for heart rate fluctuations. Chaotic behaviour is based on low-dimensional deterministic processes which are highly sensitive to initial conditions and therefore of limited predictability. Chaotic attractors produce orbits in the phase space where the points are dense. Following transformation of measured heart rate date into the phase space, such heart rate prediction characteristics were found in 6 adult rabbits. A low-dimensional deterministic model was designed by means of an artificial neural network [2*dimension of the time series +1) input neurons, 1 hidden layer, 1 output neuron and was successfully employed to predict the next 5 heart beats. Training of the neural network in terms of the prediction of the suspected chaotic orbits was dramatically improved by considering all possible prediction intervals within the prediction horizon. The trained models enabled the heart rate fluctuations to be distinguished during consciousness, under anaesthesia and additional vagal blockade.
本文的目的是利用人工神经网络识别被认为是导致心率波动的混沌吸引子。混沌行为基于低维确定性过程，这些过程对初始条件高度敏感，因此可预测性有限。混沌吸引子在相空间中产生点密集的轨道。将测量的心率数据转换到相空间后，在6只成年兔子中发现了这种心率预测特征。通过人工神经网络设计了一个低维确定性模型（[时间序列维度×2 +1]个输入神经元、1个隐藏层、1个输出神经元），并成功用于预测接下来的5次心跳。通过考虑预测范围内所有可能的预测间隔，在预测疑似混沌轨道方面，神经网络的训练得到了显著改善。经过训练的模型能够区分清醒状态、麻醉状态和额外迷走神经阻滞期间的心率波动。
