Design of spiking neural networks for blood pressure prediction during general anesthesia: considerations for optimizing results

The ability to predict blood pressure changes during general anesthesia would assist anesthetists minimize the risk of complications due to hypotensive events. However, such prediction is not trivial. Evolving spiking neural networks are a relatively new computational method that may have application to this problem. NeuCube(ST) consists of a 3-dimensional network of locally connected neurons called a Spiking Neural Network reservoir (SNNr) and can be used to classify time series data for prediction. There are a number of design considerations when using NeuCube(ST) as a classifier of time-series data: what pre-processing of the raw data is required (pre-processing), how to convert the time-series data into a spike train (input-encoding), which neurons the data are connected to (input-mapping), and how many nearest neighbours to use in classification (classification). However, it is still unclear how sensitive NeuCube(ST)-based systems are to perturbations of any of the above. In this paper we evaluate the contribution of these design factors to blood pressure prediction using NeuCube(ST). 6000 possible combinations of those NeuCube(ST) options were tested for each of 100 patients and for each a Signal to Noise Ratio was obtained. All four investigated design factors showed significant contribution to SNR. Intra-operative MAP prediction using NeuCube(ST) can be effective but performance is sensitive to the design choices.