Design of a breath analysis system for diabetes screening and blood glucose level prediction

Noninvasive Diabetes Detection through Human Breath Using TinyML-Powered E-Nose

Volatile organic compounds (VOCs) in exhaled human breath serve as pivotal biomarkers for disease identification and medical diagnostics. In the context of diabetes mellitus, the noninvasive detection of acetone, a primary biomarker using electronic noses (e-noses), has gained significant attention. However, employing e-noses requires pre-trained algorithms for precise diabetes detection, often requiring a computer with a programming environment to classify newly acquired data. This study focuses on the development of an embedded system integrating Tiny Machine Learning (TinyML) and an e-nose equipped with Metal Oxide Semiconductor (MOS) sensors for real-time diabetes detection. The study encompassed 44 individuals, comprising 22 healthy individuals and 22 diagnosed with various types of diabetes mellitus. Test results highlight the XGBoost Machine Learning algorithm's achievement of 95% detection accuracy. Additionally, the integration of deep learning algorithms, particularly deep neural networks (DNNs) and one-dimensional convolutional neural network (1D-CNN), yielded a detection efficacy of 94.44%. These outcomes underscore the potency of combining e-noses with TinyML in embedded systems, offering a noninvasive approach for diabetes mellitus detection.

Classification of Breathing Signals According to Human Motions by Combining 1D Convolutional Neural Network and Embroidered Textile Sensor

Research on healthcare and body monitoring has increased in recent years, with respiratory data being one of the most important factors. Respiratory measurements can help prevent diseases and recognize movements. Therefore, in this study, we measured respiratory data using a capacitance-based sensor garment with conductive electrodes. To determine the most stable measurement frequency, we conducted experiments using a porous Eco-flex and selected 45 kHz as the most stable frequency. Next, we trained a 1D convolutional neural network (CNN) model, which is a type of deep learning model, to classify the respiratory data according to four movements (standing, walking, fast walking, and running) using one input. The final test accuracy for classification was >95%. Therefore, the sensor garment developed in this study can measure respiratory data for four movements and classify them using deep learning, making it a versatile wearable in the form of a textile. We expect that this method will advance in various healthcare fields.

Multiple color representation and fusion for diabetes mellitus diagnosis based on back tongue images

Tongue images have been proved to be effective in diabetes mellitus (DM) diagnosis. Without requirement of collecting blood sample, tongue image based diagnosis approach is non-invasive and convenient for the patients. Meanwhile, the colors of tongues play an important in aiding accurate diagnosis. However, the tongues' colors fall on a small color gamut that makes it difficult for the existing color descripts to identify and distinguish the tiny difference of the tongues. To tackle this problem, we introduce a novel color descriptor by representing the colors with the clustering centers, namely color centroid points, of the color points sampled from tongue images. In order to boost the capacity of the descriptor, we extend it into three color spaces, i.e., RGB, HSV and LAB to mine a rich set of color information and exploit the complementary information among the three spaces. Since there exist correlation and complementarity among the features extracted from the three color spaces, we propose a novel multiple color features fusion method for DM diagnosis. Particularly, two projections are learned to project the multiple features to their corresponding shared and specific subspaces, in which their similarity and diversity are firstly measured by the Euclidean Distance and Hilbert Schmidt Independence Criterion (HSIC), respectively. To fully exploit the similar and complementary information, the two components are jointly transformed to their label vector, efficiently embedding the discriminant prior into the model, leading to significant improvement in the diagnosis outcomes. Experimental results on clinical tongue dataset substantiated the effectiveness of our proposed clustering-based color descriptor and the proposed multiple colors fusion approach. Overall, the proposed pipeline for the diagnosis of DM using back tongue images, achieved an average accuracy of up to 93.38%, indicating its potential toward realization of a clinical diagnostic tool for DM. Without loss generality, we also assessed the performance of the novel multiple features fusion method on two public datasets. The experiments prove the superiority of our multiple features learning model on general real-life application.

Missing-view completion for fatty liver disease detection

Fatty liver disease is a common disease that causes extra fat storage in an individual's liver. Patients with fatty liver disease may progress to cirrhosis and liver failure, further leading to liver cancer. The prevalence of fatty liver disease ranges from 10% to 30% in many countries. In general, detecting fatty liver requires professional neuroimaging modalities or methods such as computed tomography, ultrasound, and medical experts' practical experiences. Considering this point, finding intelligent electronic noninvasive diagnostic approaches are desired at present. Currently, most existing works in the area of computerized noninvasive disease detection often apply one view (modality) or perform multi-view (several modalities) analysis, e.g., face, tongue, and/or sublingual for disease detection. The multi-view data of patients provides more complementary information for diagnosis. However, due to the conditions of data acquisition, interference by human factors, etc., many multi-view data are defective with some missing-view information, making these multi-view data difficult to evaluate. This factor largely affects the performance of classifying disease and the development of fully computerized noninvasive methods. Thus, the purpose of this study is to address the missing view issue among noninvasive disease detection. In this work, a multi-view dataset containing facial, sublingual vein, and tongue images are initially processed to produce corresponding feature for incomplete multi-view disease diagnostic evaluation. Hereby, we propose a novel method, i.e., multi-view completion, to process the incomplete multi-view data in order to complete the missing-view information for classifying fatty liver disease from healthy candidates. In particular, this method can explore the intra-view and inter-view information to produce the missing-view data effectively. Extensive experiments on a collected dataset with 220 fatty liver patients and 220 healthy samples show that our proposed approach achieves better diagnostic results with missing-view completion compared to the original incomplete multi-view data under various classifiers. Related results prove that our method can effectively process the missing-view issue and improve the noninvasive disease detection performance.

High-Sensitivity Optical Fiber-Based Glucose Sensor Using Helical Intermediate-Period Fiber Grating

An all-fiber glucose sensor is proposed and demonstrated based on a helical intermediate-period fiber grating (HIPFG) produced by using a hydrogen/oxygen flame heating method. The HIPFG, with a grating length of 1.7 cm and a period of 35 μm, presents four sets of double dips with low insertion losses and strong coupling strengths in the transmission spectrum. The HIPFG possesses an averaged refractive index (RI) sensitivity of 213.6 nm/RIU nm/RIU in the RI range of 1.33-1.36 and a highest RI sensitivity of 472 nm/RIU at RI of 1.395. In addition, the HIPFG is demonstrated with a low-temperature sensitivity of 3.67 pm/°C, which promises a self-temperature compensation in glucose detection. In the glucose-sensing test, the HIPFG sensor manifests a detection sensitivity of 0.026 nm/(mg/mL) and a limit of detection (LOD) of 1 mg/mL. Moreover, the HIPFG sensor exhibits good stability in 2 h, indicating its capacity for long-time detection. The properties of easy fabrication, high flexibility, insensitivity to temperature, and good stability of the proposed HIPFG endow it with a promising potential for long-term and compact biosensors.

Technologies for Diabetes Self-Monitoring: A Scoping Review and Assessment Using the REASSURED Criteria

BACKGROUND

Self-management is an important pillar for diabetes control and to achieve it, glucose self-monitoring devices are needed. Currently, there exist several different devices in the market and many others are being developed. However, whether these devices are suitable to be used in resource constrained settings is yet to be evaluated.

AIMS

To assess existing glucose monitoring tools and also those in development against the REASSURED which have been previously used to evaluate diagnostic tools for communicable diseases.

METHODS

We conducted a scoping review by searching PubMed for peer-review articles published in either English, Spanish or Portuguese in the last 5 years. We selected papers including information about devices used for self-monitoring and tested on humans with diabetes; then, the REASSURED criteria were used to assess them.

RESULTS

We found a total of 7 continuous glucose monitoring device groups, 6 non-continuous, and 6 devices in development. Accuracy varied between devices and most of them were either invasive or minimally invasive. Little to no evidence is published around robustness, affordability and delivery to those in need. However, when reviewing publicly available prices, none of the devices would be affordable for people living in low- and middle-income countries.

CONCLUSIONS

Available devices cannot be considered adapted for use in self-monitoring in resource constraints settings. Further studies should aim to develop less-invasive devices that do not require a large set of components. Additionally, we suggest some improvement in the REASSURED criteria such as the inclusion of patient-important outcomes to increase its appropriateness to assess non-communicable diseases devices.

Handling non-stationarity in E-nose design: a review

Purpose

The electronic nose is an array of chemical or gas sensors and associated with a pattern-recognition framework competent in identifying and classifying odorant or non-odorant and simple or complex gases. Despite more than 30 years of research, the robust e-nose device is still limited. Most of the challenges towards reliable e-nose devices are associated with the non-stationary environment and non-stationary sensor behaviour. Data distribution of sensor array response evolves with time, referred to as non-stationarity. The purpose of this paper is to provide a comprehensive introduction to challenges related to non-stationarity in e-nose design and to review the existing literature from an application, system and algorithm perspective to provide an integrated and practical view.

Design/methodology/approach

The authors discuss the non-stationary data in general and the challenges related to the non-stationarity environment in e-nose design or non-stationary sensor behaviour. The challenges are categorised and discussed with the perspective of learning with data obtained from the sensor systems. Later, the e-nose technology is reviewed with the system, application and algorithmic point of view to discuss the current status.

Findings

The discussed challenges in e-nose design will be beneficial for researchers, as well as practitioners as it presents a comprehensive view on multiple aspects of non-stationary learning, system, algorithms and applications for e-nose. The paper presents a review of the pattern-recognition techniques, public data sets that are commonly referred to as olfactory research. Generic techniques for learning in the non-stationary environment are also presented. The authors discuss the future direction of research and major open problems related to handling non-stationarity in e-nose design.

Originality/value

The authors first time review the existing literature related to learning with e-nose in a non-stationary environment and existing generic pattern-recognition algorithms for learning in the non-stationary environment to bridge the gap between these two. The authors also present details of publicly available sensor array data sets, which will benefit the upcoming researchers in this field. The authors further emphasise several open problems and future directions, which should be considered to provide efficient solutions that can handle non-stationarity to make e-nose the next everyday device.

Review of the algorithms used in exhaled breath analysis for the detection of diabetes

Currently, intensive work is underway on the development of truly noninvasive medical diagnostic systems, including respiratory analysers based on the detection of biomarkers of several diseases including diabetes. In terms of diabetes, acetone is considered as a one of the potential biomarker, although is not the single one. Therefore, the selective detection is crucial. Most often, the analysers of exhaled breath are based on the utilization of several commercially available gas sensors or on specially designed and manufactured gas sensors to obtain the highest selectivity and sensitivity to diabetes biomarkers present in the exhaled air. An important part of each system are the algorithms that are trained to detect diabetes based on data obtained from sensor matrices. The prepared review of the literature showed that there are many limitations in the development of the versatile breath analyser, such as high metabolic variability between patients, but the results obtained by researchers using the algorithms described in this paper are very promising and most of them achieve over 90% accuracy in the detection of diabetes in exhaled air. This paper summarizes the results using various measurement systems, feature extraction and feature selection methods as well as algorithms such as Support Vector Machines, k-Nearest Neighbours and various variations of Neural Networks for the detection of diabetes in patient samples and simulated artificial breath samples.

Multitask Interactive Attention Learning Model Based on Hand Images for Assisting Chinese Medicine in Predicting Myocardial Infarction

Acute myocardial infarction (AMI) is one of the most serious and dangerous cardiovascular diseases. In recent years, the number of patients around the world has been increasing significantly, among which people under the age of 45 have become the high-risk group for sudden death of AMI. AMI occurs quickly and does not show obvious symptoms before onset. In addition, postonset clinical testing is also a complex and invasive test, which may cause some postoperative complications. Therefore, it is necessary to propose a noninvasive and convenient auxiliary diagnostic method. In traditional Chinese medicine (TCM), it is an effective auxiliary diagnostic strategy to complete the disease diagnosis through some body surface features. It is helpful to observe whether the palmar thenar undergoes hypertrophy and whether the metacarpophalangeal joint is swelling in detecting acute myocardial infarction. Combined with deep learning, we propose a depth model based on traditional palm image (MTIALM), which can help doctors of traditional Chinese medicine to predict myocardial infarction. By building the shared network, the model learns information that covers all the tasks. In addition, task-specific attention branch networks are built to simultaneously detect the symptoms of different parts of the palm. The information interaction module (IIM) is proposed to further integrate the information between task branches to ensure that the model learns as many features as possible. Experimental results show that the accuracy of our model in the detection of metacarpophalangeal joints and palmar thenar is 83.16% and 84.15%, respectively, which are significantly improved compared with the traditional classification methods.

Microwave Planar Resonant Solutions for Glucose Concentration Sensing: A Systematic Review

The measurement of glucose concentration finds interesting potential applications in both industry and biomedical contexts. Among the proposed solutions, the use of microwave planar resonant sensors has led to remarkable scientific activity during the last years. These sensors rely on the changes in the dielectric properties of the medium due to variations in the glucose concentration. These devices show electrical responses dependent on the surrounding dielectric properties, and therefore the changes in their response can be related to variations in the glucose content. This work shows an up-to-date review of this sensing approach after more than one decade of research and development. The attempts involved are sorted by the sensing parameter, and the computation of a common relative sensitivity to glucose is proposed as general comparison tool. The manuscript also discusses the key points of each sensor category and the possible future lines and challenges of the sensing approach.

Artificial Breath Classification Using XGBoost Algorithm for Diabetes Detection

Exhaled breath analysis has become more and more popular as a supplementary tool for medical diagnosis. However, the number of variables that have to be taken into account forces researchers to develop novel algorithms for proper data interpretation. This paper presents a system for analyzing exhaled air with the use of various sensors. Breath simulations with acetone as a diabetes biomarker were performed using the proposed e-nose system. The XGBoost algorithm for diabetes detection based on artificial breath analysis is presented. The results have shown that the designed system based on the XGBoost algorithm is highly selective for acetone, even at low concentrations. Moreover, in comparison with other commonly used algorithms, it was shown that XGBoost exhibits the highest performance and recall.

Computational Traditional Chinese Medicine diagnosis: A literature survey

BACKGROUND AND OBJECTIVE

Traditional Chinese Medicine (TCM) diagnosis is based on the theoretical principles and knowledge, where it is steeped in thousands of years of history to diagnose various types of diseases and syndromes. It can be generally divided into four main diagnostic approaches: 1. Inspection, 2. Auscultation and olfaction, 3. Inquiry, and 4. Palpation, which are widely used in TCM hospitals in China and around the world. With the development of intelligent computing technology in recent years, computational TCM diagnosis has grown rapidly.

METHODS

In this paper, we aim to systematically summarize the development of computational TCM diagnosis based on four diagnostic approaches, mainly focusing on digital acquisition devices, collected datasets, and computational detection approaches (algorithms). Furthermore, all related works of this field are compared and explored in detail.

RESULTS

This survey provides the principles, applications, and current progress in computing for readers and researchers in terms of computational TCM diagnosis. Moreover, the future development direction, prospect, and technological trend of computational TCM diagnosis will also be discussed in this study.

CONCLUSIONS

Recent computational TCM diagnosis works are compared in detail to show the pros/cons, where we provide some meaningful suggestions and opinions on the future research approaches in this area. This work is useful for disease detection in computational TCM diagnosis as well as health management in the smart healthcare area.

INDEX TERMS

Computational diagnosis, Traditional Chinese Medicine, survey, smart healthcare.

Accuracy of breath test for diabetes mellitus diagnosis: a systematic review and meta-analysis

The review aimed to investigate the accuracy of breath tests in the diagnosis of diabetes mellitus, identify exhaled volatile organic compounds with the most evidence as potential biomarkers, and summarize prospects and challenges in diabetic breath tests. Databases including Medline, PubMed, EMBASE, Cochrane Library and Science Citation Index Expanded were searched. Human studies describing diabetic breath analysis with more than 10 subjects as controls and patients were included. Population demographics, breath test conditions, biomarkers, analytical techniques and diagnostic accuracy were extracted. Quality assessment was performed with the Standards for Reporting Diagnostic Accuracy and a modified QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2). Forty-four research with 2699 patients with diabetes were included for qualitative data analysis and 14 eligible studies were used for meta-analysis. Pooled analysis of type 2 diabetes breath test exhibited sensitivity of 91.8% (95% CI 83.6% to 96.1%), specificity of 92.1% (95% CI 88.4% to 94.7%) and area under the curve (AUC) of 0.96 (95% CI 0.94 to 0.97). Isotopic carbon dioxide (CO₂) showed the best diagnostic accuracy with pooled sensitivity of 0.949 (95% CI 0.870 to 0.981), specificity of 0.946 (95% CI 0.891 to 0.975) and AUC of 0.98 (95% CI 0.97 to 0.99). As the most widely reported biomarker, acetone showed moderate diagnostic accuracy with pooled sensitivity of 0.638 (95% CI 0.511 to 0.748), specificity of 0.801 (95% CI 0.691 to 0.878) and AUC of 0.79 (95% CI 0.75 to 0.82). Our results indicate that breath test is a promising approach with acceptable diagnostic accuracy for diabetes mellitus and isotopic CO₂ is the optimal breath biomarker. Even so, further validation and standardization in subject control, breath sampling and analysis are still required.

pn-Heterojunction of the SWCNT/ZnO nanocomposite for temperature dependent reaction with hydrogen

A hydrogen breath test is a non-invasive and safe diagnostic tool to explore the functional gastrointestinal disorders. For the diagnosis of small intestinal bacterial overgrowth syndrome as well as carbohydrate malabsorption such as fructose, lactose, and sorbitol malabsorption, a hydrogen breath test is considered one of the gold criterions. Since the more sensitive hydrogen sensor enables the more accurate prediction about the disease, many efforts have been to the development of the high performance H₂ sensor. Herein, we fabricate the pn-junction type composite sensors using single wall carbon nanotube (SWCNT) and zinc oxide and thoroughly investigate their hydrogen sensing properties at various temperatures. We discuss the origin of sensing performance enhancement mechanism in the composite sensors, while the composite sensor with high H₂ sensing performance, linearity, repeatability, and selectivity can be prepared.

Recent Advancements and Future Prospects on E-Nose Sensors Technology and Machine Learning Approaches for Non-Invasive Diabetes Diagnosis: A Review

Diabetes mellitus, commonly measured through an invasive process which although is accurate, has manifold drawbacks especially when multiple reading are required at regular intervals. Accordingly, there is a need to develop a dependable non-invasive diabetes detection technique. Recent studies have observed that other human serums such as tears, saliva, urine and breath indicate the presence of glucose in them. These parameters open quite a few ways for non-invasive blood glucose level prediction. The analysis of a persons breath poses as a good non-invasive technique to monitor the glucose levels. It is seen that in breath, there are many bio-markers and monitoring the levels of these bio-markers indicate the possibility of various chronic diseases. Among these bio-markers, acetone a volatile organic compound found in breath has shown a good correlation to the glucose levels present in blood. Therefore, by evaluating the acetone levels in breath samples it is possible to monitor diabetes non-invasively. This paper reviews the various approaches and sensory techniques used to monitor diabetes though human breath samples.

Exhaled breath analysis by use of eNose technology: a novel diagnostic tool for interstitial lung disease

INTRODUCTION

Early and accurate diagnosis of interstitial lung diseases (ILDs) remains a major challenge. Better noninvasive diagnostic tools are much needed. We aimed to assess the accuracy of exhaled breath analysis using eNose technology to discriminate between ILD patients and healthy controls, and to distinguish ILD subgroups.

METHODS

In this cross-sectional study, exhaled breath of consecutive ILD patients and healthy controls was analysed using eNose technology (SpiroNose). Statistical analyses were done using partial least square discriminant analysis and receiver operating characteristic analysis. Independent training and validation sets (2:1) were used in larger subgroups.

RESULTS

A total of 322 ILD patients and 48 healthy controls were included: sarcoidosis (n=141), idiopathic pulmonary fibrosis (IPF) (n=85), connective tissue disease-associated ILD (n=33), chronic hypersensitivity pneumonitis (n=25), idiopathic nonspecific interstitial pneumonia (n=10), interstitial pneumonia with autoimmune features (n=11) and other ILDs (n=17). eNose sensors discriminated between ILD and healthy controls, with an area under the curve (AUC) of 1.00 in the training and validation sets. Comparison of patients with IPF and patients with other ILDs yielded an AUC of 0.91 (95% CI 0.85-0.96) in the training set and an AUC of 0.87 (95% CI 0.77-0.96) in the validation set. The eNose reliably distinguished between individual diseases, with AUC values ranging from 0.85 to 0.99.

CONCLUSIONS

eNose technology can completely distinguish ILD patients from healthy controls and can accurately discriminate between different ILD subgroups. Hence, exhaled breath analysis using eNose technology could be a novel biomarker in ILD, enabling timely diagnosis in the future.

Identification of metabolic markers in patients with type 2 Diabetes by Ultrafast gas chromatography coupled to electronic nose. A pilot study

Metabolomics is a potential tool for the discovery of new biomarkers in the early diagnosis of diseases. An ultra-fast gas chromatography system equipped to an electronic nose detector (FGC eNose) was used to identify the metabolomic profile of Volatile Organic Compounds (VOCs) in type 2 diabetes (T2D) urine from Mexican population. A cross-sectional, comparative, and clinical study with translational approach was performed. We recruited twenty T2D patients and twenty-one healthy subjects. Urine samples were taken and analyzed by FGC eNose. Eighty-eight compounds were identified through Kovats's indexes. A natural variation of 30% between the metabolites, expressed by study groups, was observed in Principal Component 1 and 2 with a significant difference (p < 0.001). The model, performed through a Canonical Analysis of Principal coordinated (CAP), allowed a correct classification of 84.6% between healthy and T2D patients, with a 15.4% error. The metabolites 2-propenal, 2-propanol, butane- 2,3-dione and 2-methylpropanal, were increased in patients with T2D, and they were strongly correlated with discrimination between clinically healthy people and T2D patients. This study identified metabolites in urine through FGC eNose that can be used as biomarkers in the identification of T2D patients. However, more studies are needed for its implementation in clinical practice.

Portable Device Based on Microwave Resonator for Noninvasive Blood Glucose Monitoring

A portable device for noninvasive blood glucose monitoring is presented. The device is based on a microwave open-loop microstrip resonator, acting as glucose sensor, following the results of a previous study. This work shows the design and development of the driving electronics, signal generation system, data processing, measurement setup and graphical user interface, to integrate the resonator into a device suitable for further experimentation in clinical scenarios. The measurement principle relies in the idea of relating the unloaded Q factor to the user's blood glucose level. An initial assessment is shown, whose results highlight some successful cases of blood glucose level tracking, and indicate the need for further research in clinical scenarios.

A Multimodal Wearable System for Continuous and Real-Time Breathing Pattern Monitoring During Daily Activity

OBJECTIVE

This study aims to understand breathing patterns during daily activities by developing a wearable respiratory and activity monitoring (WRAM) system.

METHODS

A novel multimodal fusion architecture is proposed to calculate the respiratory and exercise parameters and simultaneously identify human actions. A hybrid hierarchical classification (HHC) algorithm combining deep learning and threshold-based methods is presented to distinguish 15 complex activities for accuracy enhancement and fast computation. A series of signal processing algorithms are utilized and integrated to calculate breathing and motion indices. The designed wireless communication structure achieves the interactions among chest bands, mobile devices, and the data processing center.

RESULTS

The advantage of the proposed HHC method is evaluated by comparing the average accuracy (97.22%) and predictive time (0.0094 s) with machine learning and deep learning approaches. The nine breathing patterns during 15 activities were analyzed by investigating the data from 12 subjects. With 12 hours of naturalistic data collected from one participant, the WRAM system reports the breathing and exercise performance within the identified motions. The demonstration shows the ability of the WRAM system to monitor multiple users breathing and exercise status in real-time.

CONCLUSION

The present system demonstrates the usefulness of the framework of breathing pattern monitoring during daily activities, which may be potentially used in healthcare.

SIGNIFICANCE

The proposed multimodal based WRAM system offers new insights into the breathing function of exercise in action and presents a novel approach for precision medicine and health state monitoring.

Near-infrared tunable diode laser absorption spectroscopy-based determination of carbon dioxide in human exhaled breath

A spectroscopic detection system for the accurate monitoring of carbon dioxide (CO₂) in exhaled breath was realized by tunable diode laser absorption spectroscopy (TDLAS) in conjunction with a vertical-cavity surface-emitting laser (VCSEL) and a multipass cell with an effective optical path-length of 20 m. The VCSEL diode emitting light with an output power of 0.8 mW, covered the strong absorption line of CO₂ at 6330.82 cm^-1 by drive-current tuning. The minimum detectable concentration of 0.769‰ for CO₂ detection was obtained, and a measurement precision of approximately 100 ppm was achieved with an integration time of 168 s. Real-time online measurements were carried out for the detection of CO₂ expirograms from healthy subjects, different concentrations were obtained in dead space and alveolar gas. The exhaled CO₂ increased significantly with the increasing physical activity, reaches its maximal value at the beginning of respiratory compensation and then decreased slightly until maximal exercise. The developed measurement system has a great potential to be applied in practice for the detection of pulmonary diseases associated with CO₂ retention.

Feasibility study of portable microwave microstrip open-loop resonator for non-invasive blood glucose level sensing: proof of concept

Self-management of blood glucose level is part and parcel of diabetes treatment, which involves invasive, painful, and uncomfortable methods. A proper non-invasive blood glucose monitor (NIBGM) is therefore desirable to deal better with it. Microwave resonators can potentially be used for such a purpose. Following the positive results from an in vitro previous work, a portable device based upon a microwave resonator was developed and assessed in a multicenter proof of concept. Its electrical response was analyzed when an individual's tongue was placed onto it. The study was performed with 352 individuals during their oral glucose tolerance tests, having four measurements per individual. The findings revealed that the accuracy must be improved before the diabetes community can make real use of the device. However, the relationship between the measuring parameter and the individual's blood glucose level is coherent with that from previous works, although with higher data dispersion. This is reflected in correlation coefficients between glycemia and the measuring magnitude consistently negative, although small, for the different datasets analyzed. Further research is proposed, focused on system improvements, individual calibration, and multitechnology approach. The study of the influence of other blood components different to glucose is also advised. Graphical abstract.

Glucose Concentration Measurement in Human Blood Plasma Solutions with Microwave Sensors

Three microwave sensors are used to track the glucose level of different human blood plasma solutions. In this paper, the sensors are evaluated as glucose trackers in a context close to real human blood. Different plasma solutions sets were prepared from a human blood sample at several added glucose concentrations up to 10 wt%, adding also ascorbic acid and lactic acid at different concentrations. The experimental results for the different sensors/solutions combinations are presented in this work. The sensors show good performance and linearity as glucose level retrievers, although the sensitivities change as the rest of components vary. Different sensor behaviors depending upon the concentrations of glucose and other components are identified and characterized. The results obtained in terms of sensitivity are coherent with previous works, highlighting the contribution of glucose to the dielectric losses of the solution. The results are also consistent with the frequency evolution of the electromagnetic signature of glucose found in the literature, and are helpful for selecting frequency bands for sensing purposes and envisioning future approaches to the challenging measurement in real biological contexts. Discussion of the implications of the results and guidelines for further research and development of more accurate sensors is offered.

Wasserstein Distance Learns Domain Invariant Feature Representations for Drift Compensation of E-Nose

Electronic nose (E-nose), a kind of instrument which combines with the gas sensor and the corresponding pattern recognition algorithm, is used to detect the type and concentration of gases. However, the sensor drift will occur in realistic application scenario of E-nose, which makes a variation of data distribution in feature space and causes a decrease in prediction accuracy. Therefore, studies on the drift compensation algorithms are receiving increasing attention in the field of the E-nose. In this paper, a novel method, namely Wasserstein Distance Learned Feature Representations (WDLFR), is put forward for drift compensation, which is based on the domain invariant feature representation learning. It regards a neural network as a domain discriminator to measure the empirical Wasserstein distance between the source domain (data without drift) and target domain (drift data). The WDLFR minimizes Wasserstein distance by optimizing the feature extractor in an adversarial manner. The Wasserstein distance for domain adaption has good gradient and generalization bound. Finally, the experiments are conducted on a real dataset of E-nose from the University of California, San Diego (UCSD). The experimental results demonstrate that the effectiveness of the proposed method outperforms all compared drift compensation methods, and the WDLFR succeeds in significantly reducing the sensor drift.

Single Exhale Biomarker Breathalyzer

A single exhale breathalyzer comprises a gas sensor that satisfies the following stringent conditions: high sensitivity to the target gas, high selectivity, stable response over extended period of time and fast response. Breathalyzer implementation includes a front-end circuit matching the sensitivity of the sensor that provides the readout of the sensor signal. We present here the characterization study of the response stability and response time of a selective Nitric Oxide (NO) sensor using designed data acquisition system that also serves as a foundation for the design of wireless handheld prototype. The experimental results with the described sensor and data acquisition system demonstrate stable response to NO concentration of 200 ppb over the period of two weeks. The experiments with different injection and retraction times of the sensor exposure to constant NO concentration show a fast response time of the sensor (on the order of 15 s) and the adequate recovery time (on the order of 3 min) demonstrating suitability for the single exhale breathalyzer.

Data assimilation and multisource decision-making in systems biology based on unobtrusive Internet-of-Things devices

Biological and medical diagnoses depend on high-quality measurements. A wearable device based on Internet of Things (IoT) must be unobtrusive to the human body to encourage users to accept continuous monitoring. However, unobtrusive IoT devices are usually of low quality and unreliable because of the limitation of technology progress that has slowed down at high peak. Therefore, advanced inference techniques must be developed to address the limitations of IoT devices. This review proposes that IoT technology in biological and medical applications should be based on a new data assimilation process that fuses multiple data scales from several sources to provide diagnoses. Moreover, the required technologies are ready to support the desired disease diagnosis levels, such as hypothesis test, multiple evidence fusion, machine learning, data assimilation, and systems biology. Furthermore, cross-disciplinary integration has emerged with advancements in IoT. For example, the multiscale modeling of systems biology from proteins and cells to organs integrates current developments in biology, medicine, mathematics, engineering, artificial intelligence, and semiconductor technologies. Based on the monitoring objectives of IoT devices, researchers have gradually developed ambulant, wearable, noninvasive, unobtrusive, low-cost, and pervasive monitoring devices with data assimilation methods that can overcome the limitations of devices in terms of quality measurement. In the future, the novel features of data assimilation in systems biology and ubiquitous sensory development can describe patients' physical conditions based on few but long-term measurements.

Synergy between nanomaterials and volatile organic compounds for non-invasive medical evaluation

This article is an overview of the present and ongoing developments in the field of nanomaterial-based sensors for enabling fast, relatively inexpensive and minimally (or non-) invasive diagnostics of health conditions with follow-up by detecting volatile organic compounds (VOCs) excreted from one or combination of human body fluids and tissues (e.g., blood, urine, breath, skin). Part of the review provides a didactic examination of the concepts and approaches related to emerging sensing materials and transduction techniques linked with the VOC-based non-invasive medical evaluations. We also present and discuss diverse characteristics of these innovative sensors, such as their mode of operation, sensitivity, selectivity and response time, as well as the major approaches proposed for enhancing their ability as hybrid sensors to afford multidimensional sensing and information-based sensing. The other parts of the review give an updated compilation of the past and currently available VOC-based sensors for disease diagnostics. This compilation summarizes all VOCs identified in relation to sickness and sampling origin that links these data with advanced nanomaterial-based sensing technologies. Both strength and pitfalls are discussed and criticized, particularly from the perspective of the information and communication era. Further ideas regarding improvement of sensors, sensor arrays, sensing devices and the proposed workflow are also included.

Supervised Bayesian Source Separation of Nonlinear Mixtures for Quantitative Analysis of Gas Mixtures

In medical applications, quantitative analysis of breath may open new prospects for diagnosis or for patient monitoring. To detect acetone, a breath biomarker for diabetes, we use a single metal-oxide (MOX) gas sensor working in a dual temperature mode. We propose a linear-quadratic model to describe the mixing model mapping gas concentrations to MOX sensor responses. In this purpose, it is necessary to inverse the nonlinear problem in order to quantify the component of the gas mixture. As a proof of concept, we study a mixture of two gases, acetone and ethanol diluted in air buffer. In order to estimate the concentration of each gas, we introduce a supervised Bayesian source separation method. Based on MCMC stochastic sampling methods to estimate the mean of the posterior distribution, this Bayesian approach is robust to noise for solving this ill-posed non-linear inversion problem. We analyze the performance on a set of samples associated with a set of gas concentration covering the range suitable for exhaled breath. We use a cross-validation approach, calibrating the mixing parameters with some samples and validating the source estimation with others. Our new supervised method applied on a linear-quadratic model allows to estimate acetone and ethanol concentration with a precision of around 2 ppm.

A Linear-Quadratic Model for the Quantification of a Mixture of Two Diluted Gases with a Single Metal Oxide Sensor

The aim of our work is to quantify two gases (acetone and ethanol) diluted in an air buffer using only a single metal oxide (MOX) sensor. We took advantage of the low selectivity of the MOX sensor, exploiting a dual-temperature mode. Working at two temperatures of the MOX sensitive layer allowed us to obtain diversity in the measures. Two virtual sensors were created to characterize our gas mixture. We presented a linear-quadratic mixture sensing model which was closer to the experimental data. To validate this model and the experimental protocol, we inverted the system of quadratic equations to quantify a mixture of the two gases. The linear-quadratic model was compared to the bilinear model proposed in the literature. We presented an experimental evaluation on mixtures made of a few ppm of acetone and ethanol, and we obtained a precision close to the ppm. This is an important step towards medical applications, particularly in terms of diabetes, to deliver a non-invasive measure with a low-cost device.

Learning Domain-Invariant Subspace Using Domain Features and Independence Maximization

Domain adaptation algorithms are useful when the distributions of the training and the test data are different. In this paper, we focus on the problem of instrumental variation and time-varying drift in the field of sensors and measurement, which can be viewed as discrete and continuous distributional change in the feature space. We propose maximum independence domain adaptation (MIDA) and semi-supervised MIDA to address this problem. Domain features are first defined to describe the background information of a sample, such as the device label and acquisition time. Then, MIDA learns a subspace which has maximum independence with the domain features, so as to reduce the interdomain discrepancy in distributions. A feature augmentation strategy is also designed to project samples according to their backgrounds so as to improve the adaptation. The proposed algorithms are flexible and fast. Their effectiveness is verified by experiments on synthetic datasets and four real-world ones on sensors, measurement, and computer vision. They can greatly enhance the practicability of sensor systems, as well as extend the application scope of existing domain adaptation algorithms by uniformly handling different kinds of distributional change.

A Novel Medical E-Nose Signal Analysis System

It has been proven that certain biomarkers in people’s breath have a relationship with diseases and blood glucose levels (BGLs). As a result, it is possible to detect diseases and predict BGLs by analysis of breath samples captured by e-noses. In this paper, a novel optimized medical e-nose system specified for disease diagnosis and BGL prediction is proposed. A large-scale breath dataset has been collected using the proposed system. Experiments have been organized on the collected dataset and the experimental results have shown that the proposed system can well solve the problems of existing systems. The methods have effectively improved the classification accuracy.

Significance of Exhaled Breath Test in Clinical Diagnosis: A Special Focus on the Detection of Diabetes Mellitus

Analysis of volatile organic compounds (VOCs) emanating from human exhaled breath can provide deep insight into the status of various biochemical processes in the human body. VOCs can serve as potential biomarkers of physiological and pathophysiological conditions related to several diseases. Breath VOC analysis, a noninvasive and quick biomonitoring approach, also has potential for the early detection and progress monitoring of several diseases. This paper gives an overview of the major VOCs present in human exhaled breath, possible biochemical pathways of breath VOC generation, diagnostic importance of their analysis, and analytical techniques used in the breath test. Breath analysis relating to diabetes mellitus and its characteristic breath biomarkers is focused on. Finally, some challenges and limitations of the breath test are discussed.

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

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.

Blood glucose prediction by breath analysis system with feature selection and model fusion

It has been shown that the concentration of acetone in breath is correlated with the subject's blood glucose level (BGL). Therefore, noninvasive BGL monitoring of diabetics can be achieved by the analysis of components in breath. In this paper, a breath analysis device with 10 gas sensors is designed to measure breath samples. Transient features are extracted from the signals of the sensors. Sequential forward selection is applied on the features to find the most informative ones. In order to reduce the interference brought by the inter-subject variance of breath acetone, global and local BGL prediction models are built and fused. The two models are based on different training strategies and have different advantages. Experiments were conducted using 203 breath samples from 36 diabetic subjects. Results show that the accuracy of the proposed feature is better than other similar features and the model fusion strategy is effective. The mean absolute error and mean relative absolute error of the system are 2.07 mmol/L and 20.69%, respectively.