Abstract: The concentration of a substance, such as glucose, in a biological sample, such as human tissue (e.g. the skin of an index finger) is non-invasively determined by directing the output beam of a laser diode onto and into the skin so as to cause Raman scattering. The output of a charge coupled device, upon which the scattered light is spatially dispersed according to frequency is digitized and applied to a processor. The processor compares the Raman scattering intensity characteristics of the sample with a comparative model, in particular, an artificial neural network discriminator (ANND). The ANND is trained with a plurality of Raman spectral characteristics from biological fluids or tissue, possessing known Raman scattered light intensities versus wavelength characteristics at known concentrations. A preferred implementation of the ANND employs fuzzy adaptive resonance theory-mapping (ARTMAP), which has robust noise rejection capabilities and can readily handle nonlinear phenomena.

Abstract: A method and apparatus for sensing and classifying a condition of interest in a system from background noise in which a parameter representative of the condition of interest is sensed and an electrical signal representative of the sensed parameter is produced. The electrical signal is converted into a digital signal, this digital signal containing a signal of interest representative of the condition of interest and background noise. The digital signal is received by an artificial neural network which filters out the background noise to produce a filtered signal from the digital signal, and classifies the signal of interest from the filtered signal to produce an output representative of the classified signal.