Breath Analysis for Medical Applications

In this introductory chapter which sets the scene for this book, the background which stimulates this research work is first provided. The motivation for the focus of the work is then explained, highlighting the importance of the breath analysis used in disease diagnosis, of the development of breath analysis device, and of the design of specific pattern recognition algorithm for breath analysis. This is followed by a statement of the objective of the research, a brief summary of the work, and a general outline of the overall structure of the present study.

[1]  S. K. Vashist Non-invasive glucose monitoring technology in diabetes management: a review. , 2012, Analytica chimica acta.

[2]  Yvan Saeys,et al.  Robust Feature Selection Using Ensemble Feature Selection Techniques , 2008, ECML/PKDD.

[3]  P. Španěl,et al.  Quantitative analysis of ammonia on the breath of patients in end-stage renal failure. , 1997, Kidney international.

[4]  David Zhang,et al.  Calibration transfer and drift compensation of e-noses via coupled task learning , 2016 .

[5]  Shuzhi Sam Ge,et al.  Drift Compensation for Electronic Nose by Semi-Supervised Domain Adaption , 2014, IEEE Sensors Journal.

[6]  W. Cao,et al.  Current Status of Methods and Techniques for Breath Analysis , 2007 .

[7]  H. Byun,et al.  Analysis of diabetic patient's breath with conducting polymer sensor array , 2005 .

[8]  Taghi M. Khoshgoftaar,et al.  A review of the stability of feature selection techniques for bioinformatics data , 2012, 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI).

[9]  Qiang Yang,et al.  Adaptive Localization in a Dynamic WiFi Environment through Multi-view Learning , 2007, AAAI.

[10]  Xiaodong Wang,et al.  Classification of data from electronic nose using relevance vector machines , 2009 .

[11]  M. Phillips Method for the collection and assay of volatile organic compounds in breath. , 1997, Analytical biochemistry.

[12]  Amy Loutfi,et al.  Unsupervised feature learning for electronic nose data applied to Bacteria Identification in Blood. , 2011, NIPS 2011.

[13]  X. Zhang,et al.  Determination of acetone in human breath by gas chromatography-mass spectrometry and solid-phase microextraction with on-fiber derivatization. , 2004, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.

[14]  Yuan Shi,et al.  Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  M. Pardo,et al.  Random forests and nearest shrunken centroids for the classification of sensor array data , 2008 .

[16]  D. Blake,et al.  Exhaled methyl nitrate as a noninvasive marker of hyperglycemia in type 1 diabetes , 2007, Proceedings of the National Academy of Sciences.

[17]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Bernhard Schölkopf,et al.  Measuring Statistical Dependence with Hilbert-Schmidt Norms , 2005, ALT.

[20]  H. Abdollahi,et al.  Analysis of transient response of single quartz crystal nanobalance for determination of volatile organic compounds , 2007 .

[21]  Chun Chen,et al.  Active Learning Based on Locally Linear Reconstruction , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  A. Gutierrez-Galvez,et al.  Signal and Data Processing for Machine Olfaction and Chemical Sensing: A Review , 2012, IEEE Sensors Journal.

[23]  S J Pöppl,et al.  Predicting Type 2 diabetes using an electronic nose-based artificial neural network analysis. , 2002, Diabetes, nutrition & metabolism.

[24]  K. R. Kashwan,et al.  Robust electronic-nose system with temperature and humidity drift compensation for tea and spice flavour discrimination , 2005, 2005 Asian Conference on Sensors and the International Conference on New Techniques in Pharmaceutical and Biomedical Research.

[25]  S. Wold,et al.  Orthogonal signal correction of near-infrared spectra , 1998 .

[26]  Nicoletta Pellegrini,et al.  Colonic fermentation of indigestible carbohydrates contributes to the second-meal effect. , 2006, The American journal of clinical nutrition.

[27]  T. Mahr,et al.  Exhaled Biomarkers and Gene Expression at Preschool Age Improve Asthma Prediction at 6 Years of Age , 2015, Pediatrics.

[28]  E. Martinelli,et al.  Lung cancer identification by the analysis of breath by means of an array of non-selective gas sensors. , 2003, Biosensors & bioelectronics.

[29]  David Smith,et al.  Breath acetone concentration; biological variability and the influence of diet , 2011, Physiological measurement.

[30]  Q. Zhang,et al.  Diagnosis of diabetes by image detection of breath using gas-sensitive LAPS. , 2000, Biosensors & bioelectronics.

[31]  Yanqing Zhang,et al.  Development of Two-Stage SVM-RFE Gene Selection Strategy for Microarray Expression Data Analysis , 2007, TCBB.

[32]  Tomasz Markiewicz,et al.  Classification of milk by means of an electronic nose and SVM neural network , 2004 .

[33]  Doron Lancet,et al.  A feature extraction method for chemical sensors in electronic noses , 2003 .

[34]  Philip Drake,et al.  Real-time electronic nose based pathogen detection for respiratory intensive care patients , 2010 .

[35]  R. Dweik,et al.  Exhaled breath analysis: the new frontier in medical testing , 2008, Journal of breath research.

[36]  P. Wang,et al.  A novel method for diabetes diagnosis based on electronic nose. , 1997, Biosensors & bioelectronics.

[37]  John Blitzer,et al.  Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification , 2007, ACL.

[38]  Yongcheng Li,et al.  Joint similar and specific learning for diabetes mellitus and impaired glucose regulation detection , 2017, Inf. Sci..

[39]  Guangming Lu,et al.  Non-invasive blood glucose monitoring for diabetics by means of breath signal analysis , 2012 .

[40]  Ricardo Gutierrez-Osuna,et al.  Pattern analysis for machine olfaction: a review , 2002 .

[41]  G. Sberveglieri,et al.  Comparing the performance of different features in sensor arrays , 2007 .

[42]  D. Blake,et al.  The clinical potential of exhaled breath analysis for diabetes mellitus. , 2012, Diabetes research and clinical practice.

[43]  U. Weimar,et al.  Detection of volatile compounds correlated to human diseases through breath analysis with chemical sensors , 2002 .

[44]  Thomas G. Dietterich,et al.  Advances in neural information processing systems : proceedings of the ... conference , 1989 .

[45]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[46]  Bogdan Gabrys,et al.  Review of adaptation mechanisms for data-driven soft sensors , 2011, Comput. Chem. Eng..

[47]  Hao Helen Zhang,et al.  Consistent Group Identification and Variable Selection in Regression With Correlated Predictors , 2013, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.

[48]  Ji Haibo,et al.  Near-infrared calibration transfer via support vector machine and transfer learning , 2015 .

[49]  P. Barnes,et al.  Exhaled carbon monoxide levels elevated in diabetes and correlated with glucose concentration in blood: a new test for monitoring the disease? , 1999, Chest.

[50]  Yoshua Bengio,et al.  Deep Learning of Representations for Unsupervised and Transfer Learning , 2011, ICML Unsupervised and Transfer Learning.

[51]  Ivor W. Tsang,et al.  Hybrid Heterogeneous Transfer Learning through Deep Learning , 2014, AAAI.

[52]  Ke Huang,et al.  Sparse Representation for Signal Classification , 2006, NIPS.

[53]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[54]  Yoshua Bengio,et al.  Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach , 2011, ICML.

[55]  G. Rooth,et al.  Acetone in alveolar air, and the control of diabetes. , 1966, Lancet.

[56]  S. Hosseini-Golgoo,et al.  Assessing the diagnostic information in the response patterns of a temperature-modulated tin oxide gas sensor , 2011 .

[57]  Fengchun Tian,et al.  n-line sensor calibration transfer among electronic nose instruments for onitoring volatile organic chemicals in indoor air quality , 2011 .

[58]  M. Shepherd,et al.  A Study on Breath Acetone in Diabetic Patients Using a Cavity Ringdown Breath Analyzer: Exploring Correlations of Breath Acetone With Blood Glucose and Glycohemoglobin A1C , 2010, IEEE Sensors Journal.

[59]  Fredrik Winquist,et al.  Extraction and selection of parameters for evaluation of breath alcohol measurement with an electronic nose , 2000 .

[60]  David Zhang,et al.  Monitor blood glucose levels via breath analysis system and Sparse Representation approach , 2010, 2010 IEEE Sensors.

[61]  T R Fraser,et al.  Breath acetone and blood sugar measurements in diabetes. , 1969, Clinical science.

[62]  Trevor Hastie,et al.  Averaged gene expressions for regression. , 2007, Biostatistics.

[63]  I. Horváth,et al.  Increased levels of exhaled carbon monoxide in bronchiectasis: a new marker of oxidative stress , 1998, Thorax.

[64]  P. Sterk,et al.  Smelling the Diagnosis: The Electronic Nose as Diagnostic Tool in Inflammatory Arthritis. A Case-Reference Study , 2016, PloS one.

[65]  J. Austin,et al.  Prediction of lung cancer using volatile biomarkers in breath. , 2007, Cancer biomarkers : section A of Disease markers.

[66]  H. Haick,et al.  Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules , 2016, ACS nano.

[67]  Ali Osman Selvi,et al.  Electronic Nose System Based on Quartz Crystal Microbalance Sensor for Blood Glucose and HbA1c Levels From Exhaled Breath Odor , 2013, IEEE Sensors Journal.

[68]  Min Jiang,et al.  Integration of Global and Local Metrics for Domain Adaptation Learning Via Dimensionality Reduction , 2017, IEEE Transactions on Cybernetics.

[69]  W. Miekisch,et al.  Diagnostic potential of breath analysis--focus on volatile organic compounds. , 2004, Clinica chimica acta; international journal of clinical chemistry.

[70]  Sejong Yoon,et al.  Mutual information-based SVM-RFE for diagnostic classification of digitized mammograms , 2009, Pattern Recognit. Lett..

[71]  N. Bârsan,et al.  Electronic nose: current status and future trends. , 2008, Chemical reviews.

[72]  Melanie Hilario,et al.  Stability of feature selection algorithms , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[73]  Gian Carlo Cardinali,et al.  An electronic nose based on solid state sensor arrays for low-cost indoor air quality monitoring applications , 2004 .

[74]  Yuan Shi,et al.  Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation , 2012, ICML.

[75]  M. J. Sulway,et al.  Acetone in diabetic ketoacidosis. , 1970, Lancet.

[76]  Sumit Chopra,et al.  DLID: Deep Learning for Domain Adaptation by Interpolating between Domains , 2013 .

[77]  D. Blake,et al.  Improved predictive models for plasma glucose estimation from multi-linear regression analysis of exhaled volatile organic compounds. , 2009, Journal of applied physiology.

[78]  Onofrio Resta,et al.  An electronic nose in the discrimination of patients with non-small cell lung cancer and COPD. , 2009, Lung cancer.

[79]  R. Gutierrez-Osunaa,et al.  Transient response analysis for temperature-modulated chemoresistors , 2003 .

[80]  Alexandre Perera,et al.  Drift compensation of gas sensor array data by Orthogonal Signal Correction , 2010 .

[81]  Martin Liess,et al.  Electric-field-induced migration of chemisorbed gas molecules on a sensitive film—a new chemical sensor , 2002 .

[82]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[83]  L. T. McGrath,et al.  Breath isoprene during acute respiratory exacerbation in cystic fibrosis. , 2000, The European respiratory journal.

[84]  Kiyokatsu Jinno,et al.  Breath acetone analysis with miniaturized sample preparation device: in-needle preconcentration and subsequent determination by gas chromatography-mass spectroscopy. , 2009, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.

[85]  J. Brezmes,et al.  Variable selection for support vector machine based multisensor systems , 2007 .

[86]  David Zhang,et al.  Diabetes Identification and Classification by Means of a Breath Analysis System , 2010, ICMB.

[87]  Mikhail Belkin,et al.  Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..

[88]  M. Phillips,et al.  Volatile Markers of Breast Cancer in the Breath , 2003, The breast journal.

[89]  Olaf Tietje,et al.  Prediction of lung cancer using volatile biomarkers in breath , 2005 .

[90]  J.C. Rajapakse,et al.  SVM-RFE With MRMR Filter for Gene Selection , 2010, IEEE Transactions on NanoBioscience.

[91]  D M Hansell,et al.  Elevated levels of exhaled nitric oxide in bronchiectasis. , 1995, American journal of respiratory and critical care medicine.

[92]  E. Martinelli,et al.  Feature Extraction of chemical sensors in phase space , 2003 .

[93]  M. Phillips,et al.  Heart allograft rejection: detection with breath alkanes in low levels (the HARDBALL study). , 2004, The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation.

[94]  David Zhang,et al.  Sensor Evaluation in a Breath Analysis System , 2014, 2014 International Conference on Medical Biometrics.

[95]  M. Phillips,et al.  Increased breath biomarkers of oxidative stress in diabetes mellitus. , 2004, Clinica chimica acta; international journal of clinical chemistry.

[96]  W. Maziak,et al.  Exhaled nitric oxide in chronic obstructive pulmonary disease. , 1998, American journal of respiratory and critical care medicine.

[97]  Tan Yee Fan,et al.  A Tutorial on Support Vector Machine , 2009 .

[98]  M. Ledochowski,et al.  Implementation and interpretation of hydrogen breath tests , 2008, Journal of breath research.

[99]  Le Song,et al.  Colored Maximum Variance Unfolding , 2007, NIPS.

[100]  S Lenzi,et al.  Monitoring breath during oral glucose tolerance tests , 2013, Journal of breath research.

[101]  Peter J Sterk,et al.  An electronic nose in the discrimination of patients with asthma and controls. , 2007, The Journal of allergy and clinical immunology.

[102]  Edward J. Wolfrum,et al.  Metal Oxide Sensor Arrays for the Detection, Differentiation, and Quantification of Volatile Organic Compounds at Sub-Parts-Per-Million Concentration Levels , 2006 .

[103]  C. Distante,et al.  On the study of feature extraction methods for an electronic nose , 2002 .

[104]  David Zhang,et al.  Blood glucose prediction by breath analysis system with feature selection and model fusion , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[105]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[106]  L. G. Kwong,et al.  Data analysis for a hybrid sensor array , 2005 .

[107]  Amnon Shashua,et al.  Ranking with Large Margin Principle: Two Approaches , 2002, NIPS.

[108]  S LEVEY,et al.  STUDIES OF METABOLIC PRODUCTS IN EXPIRED AIR. II. ACETONE. , 1964, The Journal of laboratory and clinical medicine.

[109]  Ivor W. Tsang,et al.  Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.

[110]  Andrea Bonarini,et al.  Lung Cancer Identification by an Electronic Nose based on an Array of MOS Sensors , 2007, 2007 International Joint Conference on Neural Networks.

[111]  David Zhang,et al.  Correcting Instrumental Variation and Time-Varying Drift: A Transfer Learning Approach With Autoencoders , 2016, IEEE Transactions on Instrumentation and Measurement.

[112]  Wei Chu,et al.  New approaches to support vector ordinal regression , 2005, ICML.

[113]  Björn W. Schuller,et al.  Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion Recognition , 2014, IEEE Signal Processing Letters.

[114]  Sheng-Fu Liang,et al.  Identification of Schizophrenic Patients and Healthy Controls Based on Musical Perception Using AEP Analysis , 2018 .

[115]  Qi Ye,et al.  Classification of multiple indoor air contaminants by an electronic nose and a hybrid support vector machine , 2012 .

[116]  H. Paretzke,et al.  Differences in exhaled gas profiles between patients with type 2 diabetes and healthy controls. , 2010, Diabetes technology & therapeutics.

[117]  Amy Loutfi,et al.  A review of unsupervised feature learning and deep learning for time-series modeling , 2014, Pattern Recognit. Lett..

[118]  Eduard Llobet,et al.  Efficient feature selection for mass spectrometry based electronic nose applications , 2007 .

[119]  Shiguang Shan,et al.  Stacked Progressive Auto-Encoders (SPAE) for Face Recognition Across Poses , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[120]  E. Israel,et al.  Exhaled nitric oxide as a diagnostic test for asthma: online versus offline techniques and effect of flow rate. , 2002, American journal of respiratory and critical care medicine.

[121]  Rishemjit Kaur,et al.  A novel approach using Dynamic Social Impact Theory for optimization of impedance-Tongue (iTongue) , 2011 .

[122]  Claire Turner,et al.  Potential of breath and skin analysis for monitoring blood glucose concentration in diabetes , 2011, Expert review of molecular diagnostics.

[123]  Matteo Falasconi,et al.  Drift Correction Methods for Gas Chemical Sensors in Artificial Olfaction Systems: Techniques and Challenges , 2012 .

[124]  Jana Novovicová,et al.  Evaluating Stability and Comparing Output of Feature Selectors that Optimize Feature Subset Cardinality , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[125]  David Zhang,et al.  Drift Correction Using Maximum Independence Domain Adaptation , 2017 .

[126]  D. Zhang,et al.  A Breath Analysis System for Diabetes Screening and Blood Glucose Level Prediction , 2017 .

[127]  K. Müller,et al.  Finding stationary subspaces in multivariate time series. , 2009, Physical review letters.

[128]  Yuh-Jiuan Lin,et al.  Application of the electronic nose for uremia diagnosis , 2001 .

[129]  A. Ceccarini,et al.  Breath analysis: trends in techniques and clinical applications , 2005 .

[130]  Giorgio Pennazza,et al.  An investigation on electronic nose diagnosis of lung cancer. , 2010, Lung cancer.

[131]  Shankar Vembu,et al.  Chemical gas sensor drift compensation using classifier ensembles , 2012 .

[132]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[133]  A. Bifet,et al.  A survey on concept drift adaptation , 2014, ACM Comput. Surv..

[134]  R. Capuano,et al.  Solid-state gas sensors for breath analysis: a review. , 2014, Analytica chimica acta.

[135]  Pradeep Kurup,et al.  Decision tree approach for classification and dimensionality reduction of electronic nose data , 2011 .

[136]  Rishemjit Kaur,et al.  Enhancing electronic nose performance: A novel feature selection approach using dynamic social impact theory and moving window time slicing for classification of Kangra orthodox black tea (Camellia sinensis (L.) O. Kuntze) , 2012 .

[137]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[138]  Ming Shao,et al.  Generalized Transfer Subspace Learning Through Low-Rank Constraint , 2014, International Journal of Computer Vision.

[139]  C. Xie,et al.  An entire feature extraction method of metal oxide gas sensors , 2008 .

[140]  David Zhang,et al.  A novel breath analysis system for diabetes diagnosis , 2012, 2012 International Conference on Computerized Healthcare (ICCH).

[141]  Terence H Risby,et al.  Breath biomarkers for detection of human liver diseases: preliminary study , 2002, Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals.

[142]  H. Haick,et al.  Combined Volatolomics for Monitoring of Human Body Chemistry , 2014, Scientific Reports.

[143]  Massimiliano Pontil,et al.  Regularized multi--task learning , 2004, KDD.

[144]  M. Phillips,et al.  Increased pentane and carbon disulfide in the breath of patients with schizophrenia. , 1993, Journal of clinical pathology.

[145]  Simone Meinardi,et al.  Breath ethanol and acetone as indicators of serum glucose levels: an initial report. , 2005, Diabetes technology & therapeutics.

[146]  Fuzhen Zhuang,et al.  Supervised Representation Learning: Transfer Learning with Deep Autoencoders , 2015, IJCAI.

[147]  Anne-Claude Romain,et al.  Long Term Stability Of Metal Oxide-Based Gas Sensors For E-nose Environmental Applications: an overview , 2009 .

[148]  David Zhang,et al.  Design of a Breath Analysis System for Diabetes Screening and Blood Glucose Level Prediction , 2014, IEEE Transactions on Biomedical Engineering.

[149]  M. Santonico,et al.  Olfactory systems for medical applications , 2008 .

[150]  Jinbo Bi,et al.  Active learning via transductive experimental design , 2006, ICML.

[151]  Kilian Q. Weinberger,et al.  Marginalized Denoising Autoencoders for Domain Adaptation , 2012, ICML.

[152]  M. Sjöström,et al.  Drift correction for gas sensors using multivariate methods , 2000 .

[153]  J. Deslypere,et al.  A new non-invasive technology to screen for dysglycaemia including diabetes. , 2010, Diabetes research and clinical practice.

[154]  M. C. Horrillo,et al.  Identification of typical wine aromas by means of an electronic nose , 2004, Proceedings of IEEE Sensors, 2004..

[155]  M. Pardo,et al.  Classification of electronic nose data with support vector machines , 2005 .

[156]  F. Azuaje,et al.  Multiple SVM-RFE for gene selection in cancer classification with expression data , 2005, IEEE Transactions on NanoBioscience.

[157]  T. H. Risby CURRENT STATUS OF CLINICAL BREATH ANALYSIS , 2005 .

[158]  Amir Amini,et al.  Improving gas identification accuracy of a temperature-modulated gas sensor using an ensemble of classifiers , 2013 .

[159]  David Zhang,et al.  Domain Adaptation Extreme Learning Machines for Drift Compensation in E-Nose Systems , 2015, IEEE Transactions on Instrumentation and Measurement.

[160]  Rama Chellappa,et al.  Visual Domain Adaptation: A survey of recent advances , 2015, IEEE Signal Processing Magazine.

[161]  Changsheng Xie,et al.  ‘Sensory analysis’ of Chinese vinegars using an electronic nose , 2008 .

[162]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[163]  L. Laffel Ketone bodies: a review of physiology, pathophysiology and application of monitoring to diabetes , 1999, Diabetes/metabolism research and reviews.

[164]  Alain Rakotomamonjy,et al.  Variable Selection Using SVM-based Criteria , 2003, J. Mach. Learn. Res..

[165]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[166]  David Zhang,et al.  Improving the transfer ability of prediction models for electronic noses , 2015 .

[167]  David Zhang,et al.  Learning Domain-Invariant Subspace Using Domain Features and Independence Maximization , 2016, IEEE Transactions on Cybernetics.

[168]  David Zhang,et al.  Feature selection and analysis on correlated gas sensor data with recursive feature elimination , 2015 .

[169]  Xuelong Li,et al.  Flowing on Riemannian Manifold: Domain Adaptation by Shifting Covariance , 2014, IEEE Transactions on Cybernetics.

[170]  Luís A. Alexandre,et al.  Improving Deep Neural Network Performance by Reusing Features Trained with Transductive Transference , 2014, ICANN.

[171]  Alphus D. Wilson,et al.  Advances in Electronic-Nose Technologies Developed for Biomedical Applications , 2011, Sensors.

[172]  E. Wouters,et al.  Development of accurate classification method based on the analysis of volatile organic compounds from human exhaled air. , 2008, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.

[173]  A. Maran,et al.  Non-invasive glucose monitoring: assessment of technologies and devices according to quantitative criteria. , 2007, Diabetes research and clinical practice.

[174]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[175]  Carlos Eduardo Ferrante do Amaral,et al.  Current development in non-invasive glucose monitoring. , 2008, Medical engineering & physics.

[176]  Thomas Lengauer,et al.  Classification with correlated features: unreliability of feature ranking and solutions , 2011, Bioinform..

[177]  Christopher Walton,et al.  Breath acetone concentration decreases with blood glucose concentration in type I diabetes mellitus patients during hypoglycaemic clamps , 2009, Journal of breath research.

[178]  S. Pratsinis,et al.  Correlations between blood glucose and breath components from portable gas sensors and PTR-TOF-MS , 2013, Journal of breath research.

[179]  M. Phillips,et al.  Effect of age on the breath methylated alkane contour, a display of apparent new markers of oxidative stress. , 2000, The Journal of laboratory and clinical medicine.

[180]  D. Goldstein,et al.  Defining the relationship between plasma glucose and HbA(1c): analysis of glucose profiles and HbA(1c) in the Diabetes Control and Complications Trial. , 2002, Diabetes care.

[181]  L. Marchand,et al.  Breath hydrogen and methane in populations at different risk for colon cancer , 1993, International journal of cancer.

[182]  Zohreh Azimifar,et al.  Supervised principal component analysis: Visualization, classification and regression on subspaces and submanifolds , 2011, Pattern Recognit..

[183]  Steven D. Brown,et al.  Transfer of multivariate calibration models: a review , 2002 .

[184]  A. Hierlemann,et al.  Higher-order Chemical Sensing , 2007 .

[185]  Klaus Geiger,et al.  Breath analysis in critically ill patients: potential and limitations , 2004, Expert review of molecular diagnostics.

[186]  David Zhang,et al.  A Novel Breath Analysis System Based on Electronic Olfaction , 2010, IEEE Transactions on Biomedical Engineering.

[187]  B. Kremer,et al.  Differentiating head and neck carcinoma from lung carcinoma with an electronic nose: a proof of concept study , 2016, European Archives of Oto-Rhino-Laryngology.

[188]  Tinne Tuytelaars,et al.  Unsupervised Visual Domain Adaptation Using Subspace Alignment , 2013, 2013 IEEE International Conference on Computer Vision.

[189]  David Zhang,et al.  An Optimized Tongue Image Color Correction Scheme , 2010, IEEE Transactions on Information Technology in Biomedicine.

[190]  Eugenio Baraldi,et al.  Exhaled NO and breath condensate. , 2006, Paediatric respiratory reviews.

[191]  Silvia Coradeschi,et al.  Direct Identification of Bacteria in Blood Culture Samples Using an Electronic Nose , 2010, IEEE Transactions on Biomedical Engineering.

[192]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.