Dynamic monitoring and control of patient anaesthetic and dose levels: time-delay, moving-average neural networks, and principal components analysis
The goal of this study was to examine the capabilities of neural network models for dynamic monitoring and control of patient anaesthetic and dose levels. The network models that we considered are split into two basic groups: static networks and dynamic networks. Static networks are characterised by equations that are memoryless. On the other hand, dynamic networks are systems with memory. Additionally, principal components analysis was used to introduce a further improvement to network design by reducing the dimensionality of the encoded temporal information. Principal components analysis was applied as both pre-processing and post-processing techniques. In the first instance it was used to reduce the dimensionality of the data to more manageable intrinsic information. In the second instance it was employed to understand how the hidden layers separate the data, in order to optimise the network architecture.
moving-average principal components analysis static networks dynamic networks anaesthesia time-delay neural networks temporal pattern processing