Titlebook: Breath Analysis for Medical Applications; David Zhang,Dongmin Guo,Ke Yan Book 2017 Springer Nature Singapore Pte Ltd. 2017 Breath signal D

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Ikujiro Nonaka,Ichiro Yamaguchimodel and correct these influential factors explicitly with the help of transfer samples. It generates drift-corrected and discriminative representation of the original data, which can then be applied to various prediction algorithms. Experimental results show that DCAE outperforms typical drift cor

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https://doi.org/10.1007/978-981-16-6851-7 unsupervised domain adaptation approaches. Maximum independence domain adaptation (MIDA) is proposed in this chapter for unsupervised drift correction. MIDA borrows the definition of domain features in the last chapter and learns features which have maximal independence with them, so as to reduce t

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Visionen: Das Unmögliche denkenith the blood glucose level of patients. Therefore, the acetone in human breath can be used to monitor the development of diabetes. In this chapter, we introduce a breath analysis system to measure acetone in human breath, and therefore to evaluate the blood glucose levels of diabetics. The system s

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Unternehmer sind die besseren Mathematiker,e with abnormal concentrations and the concentrations rise gradually with patients’ blood glucose values. Therefore, the acetone in human breath can be used to monitor the development of diabetes. This chapter investigates the potential of breath signals analysis as a way for blood glucose monitorin

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and potential medical applications of computerized breath diagnosis. It is a valuable interdisciplinary resource for researchers, professionals and postgraduate students working in various fields, including breath diagnosis, signal processing, pattern recognition, and biometrics..978-981-13-5106-8978-981-10-4322-2