Sandham, William, Nikoletou, Dimitra, Hamilton, David, Paterson, Ken, Japp, Alan and McGregor, Catriona (1998) Blood glucose prediction for diabetes therapy using a recurrent artificial neural network. In: EUSIPCO-98: Ninth European Signal Processing Conference; 08-11 Sep 1998, Rhodes, Greece.

Abstract

Expert short-term management of diabetes through good glycaemic control, is necessary to delay or even prevent serious degenerative complications developing in the long term, due to consistently high blood glucose levels (BGLs). Good glycaemic control may be achieved by predicting a future BGL based on past BGLs and past and anticipated diet, exercise schedule and insulin regime (the latter for insulin dependent diabetics). This predicted BGL may then be used in a computerised management system to achieve short-term normoglycaemia. This paper investigates the use of a recurrent artificial neural network for predicting BGL, and presents preliminary results for two insulin dependent diabetic females.

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