NDLI: Expectation-maximization-estimation of mixture densities for Electron-Spin-Resonance-analysis of albumin

Content Provider	IEEE Xplore Digital Library
Author	Schmidt, C. Krumbiegel, C. Waterstradt, K. Petznick, G. Schafer, H. Schnurr, K.
Copyright Year	2009
Description	Author affiliation: MedInnovation GmbH, Freiheitstr. 124/126, 15745 Wildau, Germany (Schmidt, C.; Waterstradt, K.; Schafer, H.; Schnurr, K.) \|\| Department of Bioinformatics, University of Applied Sciences Wildau, Bahnhofstr.1, 15745, Germany (Krumbiegel, C.; Petznick, G.)
Abstract	Early diagnosis of human cancer is of crucial importance for successful therapies. Cancer diagnosis via ESR (Electron-Spin-Resonance) spectroscopy of albumin found in human blood provides a new promising approach. The ESR frontend signal processing follows a protocol of our proprietary ‘mobility of molecular structure test’ (MMS-Test) and provides a real-valued 33-dimensional vector representation per sample, which combines a representative feature set of the binding ability (spin-probes) of albumin under investigation. Classical statistical pattern recognition is then applied to the feature vector, including LDA and EM mixture density estimation, leading to a classification error rate of 14% between the two patient classes ‘healthy’ and ‘suspect’. The class ‘suspect’ includes cancer and other chronic condition. The investigation was performed on a proprietary database of MedInnovation with 1176 cancer and non-cancer patients.
Starting Page	1
Ending Page	4
File Size	532968
Page Count	4
File Format	PDF
ISBN	9781424447619
DOI	10.1109/GENSIPS.2009.5174354
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Spectroscopy Paramagnetic resonance Protocols Humans Medical treatment Pattern recognition Blood Mixture Densities Expectation Maximization Estimation MMS-Test Linear Discriminant Analysis Signal processing ESR Spectroscopy Cancer Testing Early Cancer Diagnosis
Content Type	Text
Resource Type	Article

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