Diabetes Identification and Classification by Means of a Breath Analysis System

Guo, Dongmin; Zhang, David; Li, Naimin; Yang, Jianhua

doi:10.1007/978-3-642-13923-9_6

Cited by 18 publications

(15 citation statements)

References 15 publications

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“…The results indicated that the system was not only able to distinguish between breath samples from patients with diabetes and healthy subjects, but also to represent the fluctuation of blood glucose of diabetics. In the same year, Guo et al [40] improved accuracy of diabetes condition monitoring by using a SRC method. Coupled with SRC, the system was able to classify these levels with a much better accuracy than the accuracy reported in [41].…”

Section: Breath Biomarker and Diseasesmentioning

confidence: 99%

Breath Analysis for Detecting Diseases on Respiratory, Metabolic and Digestive System

Kou¹,

Zhang²,

You³

et al. 2019

JBiSE

Self Cite

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Recently, biological technology and computer science are of great importance in medical applications. Since one's breath biomarkers have been proved to be related with diseases, it is possible to detect diseases by analysis of breath samples captured by e-noses. In this paper, a novel medical e-nose system specific to disease diagnosis was used to collect a large-scale breath dataset. Methods for signal processing, feature extracting as well as feature & sensor selection were discussed for detecting diseases on respiratory, metabolic and digestive system. Sequential forward selection is used to select the best combination of sensors and features. The experimental results showed that the proposed system was able to well distinguish healthy samples and samples with different diseases. The results also showed the most significant sensors and features for different tasks, which meets the relationship between diseases and breath biomarkers. By selecting best combination of different sensors and features for different tasks, the e-nose system is shown to be helpful and effective for diseases diagnosis on respiratory, metabolic and digestive system. Open Access https://doi.org/10.4236/jbise.2019.121004 40 J. Biomedical Science and Engineering Breath Analysis with E-NoseCurrently, the measurement of exhaled breath is usually performed by two common gas analysis apparatuses, gas chromatography (GC) [26] or electronic nose (e-nose) [27]. GC can separate and identify molecules that are responsible for typical odors occurring in specific diseases, which is very accurate for

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Section: Breath Biomarker and Diseasesmentioning

confidence: 99%

Breath Analysis for Detecting Diseases on Respiratory, Metabolic and Digestive System

Kou¹,

Zhang²,

You³

et al. 2019

JBiSE

Self Cite

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“…The diabetes patients were discriminated from normal persons by the principle component analysis (PCA) with k nearest neighbor classifier. Another breath analysis system [ 56 , 57 ] was established for two purposes: diabetes diagnosis and blood glucose levels prediction. Their diagnosis models were built by support vector classifier and support vector regression, respectively.…”

Section: Machine Learning Approaches For Tcm Patient Classificatiomentioning

confidence: 99%

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

Zhao

Wang

et al. 2015

Evidence-Based Complementary and Alternative Medicine

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As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification.

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“…low, boundary, high and very high blood glucose conc.) was possible between 50-75% of the time (74). Whilst this is someway off the required sensitivity levels, the potential of a noninvasive method for frequent blood glucose monitoring negating the potentials hazards of blood sampling is obvious.…”

Section: Breath Analysis Applied To Renal and Hepatic Diagnosismentioning

confidence: 99%

Breath Analysis for Medical Diagnosis

Hill

Binions

2012

International Journal on Smart Sensing and Intelligent Systems

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The purpose of this review is to highlight the advances in technology and understanding in the field of breath analysis for medical diagnosis. A critical review of the methods of breath collection, treatment, and analysis is given, highlighting the problems facing the field and areas where promising advancement has been made. One particular area of interest is centered around electronic noses, ideally, portable devices which aim to mimic biological olifactory systems in analysing gases to produce odor fingerprints. Furthermore, recent work has shown it is possible to modify the basic sensor materials to both improve their performance, increase their tolerance to factors such as water vapour interferance which often leave the sensor system de-sensitized to the gaseous biomarkers, and enhance their selectivity. It will be shown how it is possible to accurately quantify concentrations of VOC's INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS, VOL. 5, NO. 2, JUNE 2012 401 and make disease diagnosis from analysis of the collected data which compare favorably with traditional medical diagnostic techniques.

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Diabetes Identification and Classification by Means of a Breath Analysis System

Cited by 18 publications

References 15 publications

Breath Analysis for Detecting Diseases on Respiratory, Metabolic and Digestive System

Breath Analysis for Detecting Diseases on Respiratory, Metabolic and Digestive System

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

Breath Analysis for Medical Diagnosis

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