J Med Syst - Noninvasive blood glucose sensing using near infra-red spectroscopy and artificial neural networks based on inverse delayed function model of neuron.

Tópicos

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Resumo

In this paper, a non-invasive blood glucose sensing system is presented using near infra-red(NIR) spectroscopy. The signal from the NIR optodes is processed using artificial neural networks (ANN) to estimate the glucose level in blood. In order to obtain accurate values of the synaptic weights of the ANN, inverse delayed (ID) function model of neuron has been used. The ANN model has been implemented on field programmable gate array (FPGA). Error in estimating glucose levels using ANN based on ID function model of neuron implemented on FPGA, came out to be 1.02 mg/dl using 15 hidden neurons in the hidden layer as against 5.48 mg/dl using ANN based on conventional neuron model.

Resumo Limpo

paper noninvas blood glucos sens system present use near infrarednir spectroscopi signal nir optod process use artifici neural network ann estim glucos level blood order obtain accur valu synapt weight ann invers delay id function model neuron use ann model implement field programm gate array fpga error estim glucos level use ann base id function model neuron implement fpga came mgdl use hidden neuron hidden layer mgdl use ann base convent neuron model

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