Sparse Coding Neural Gas for Analysis of Nuclear Magnetic Resonance Spectroscopy

Nuclear magnetic resonance spectroscopy is a technique for the analysis of complex biochemical materials. Thereby the identification of known sub-patterns is important. These measurements require an accurate preprocessing and analysis to meet clinical standards. Here we present a method for an appropriate sparse encoding of NMR spectral data combined with a fuzzy classification system allowing the identification of sub-patterns including mixtures thereof. The method is evaluated in contrast to an alternative approach using simulated metabolic spectra.