Low-Cost Lung Cancer Detection Using Machine Learning on Breath Samples

In recent years, electronic nose devices have become a popular approach for identifying respiratory disorders including lung cancer. Traditional e-nose systems have had very consistent principles and patterns of sensor responses. After coming to the realization that detecting cancer at early stages can save 99 percent of lives, it has become imperative to design a machine that can easily detect for lung cancer(the most common type of cancer) in a cost-effective and accurate way. Designing an Al Nose was a perfect way to counteract the problem. A tiny e-nose system with 14 gas sensors of four types was created and fifty breath samples were analyzed. Five feature extraction techniques and two classifiers were used to test the system's efficiency in recognizing and discriminating lung cancer from other respiratory disorders and healthy controls. Finally, the impact of different sensor types on the capacity of e-nose systems to identify objects was investigated. The sensitivity, specificity, and accuracy of distinguishing lung cancer patients from healthy controls are 91.58 percent, 91.72 percent, and 91.59 percent, respectively, when utilizing the DA fuzzy 5-NIN classification approach. The findings imply that type-specific sensors might improve the diagnostic accuracy of e-nose devices greatly. These findings indicated that the e-nose system described in this work might be used in lung cancer screening with good results. Furthermore, while creating e-nose systems, it is critical to consider type-different sensors. This machine covers all aspects to most effectively develop a machine that can detect lung cancer that is user-friendly and low cost

Identification of suitable biomarkers for stress and emotion detection for future personal affective wearable sensors

Skin conductivity (i.e., sweat) forms the basis of many physiology-based emotion and stress detection systems. However, such systems typically do not detect the biomarkers present in sweat, and thus do not take advantage of the biological information in the sweat. Likewise, such systems do not detect the volatile organic components (VOC’s) created under stressful conditions. This work presents a review into the current status of human emotional stress biomarkers and proposes the major potential biomarkers for future wearable sensors in affective systems. Emotional stress has been classified as a major contributor in several social problems, related to crime, health, the economy, and indeed quality of life. While blood cortisol tests, electroencephalography and physiological parameter methods are the gold standards for measuring stress; however, they are typically invasive or inconvenient and not suitable for wearable real-time stress monitoring. Alternatively, cortisol in biofluids and VOCs emitted from the skin appear to be practical and useful markers for sensors to detect emotional stress events. This work has identified antistress hormones and cortisol metabolites as the primary stress biomarkers that can be used in future sensors for wearable affective systems

Breathomics—exhaled volatile organic compound analysis to detect hepatic encephalopathy : a pilot study

he current diagnostic challenge with diagnosing hepatic encephalopathy (HE) is identifying those with minimal HE as opposed to the more clinically apparent covert/overt HE. Rifaximin, is an effective therapy but earlier identification and treatment of HE could prevent liver disease progression and hospitalization. Our pilot study aimed to analyse breath samples of patients with different HE grades, and controls, using a portable electronic (e) nose. 42 patients were enrolled; 22 with HE and 20 controls. Bedside breath samples were captured and analysed using an uvFAIMS machine (portable e-nose). West Haven criteria applied and MELD scores calculated. We classify HE patients from controls with a sensitivity and specificity of 0.88 (0.73-0.95) and 0.68 (0.51-0.81) respectively, AUROC 0.84 (0.75-0.93). Minimal HE was distinguishable from covert/overt HE with sensitivity of 0.79 and specificity of 0.5, AUROC 0.71 (0.57-0.84). This pilot study has highlighted the potential of breathomics to identify VOCs signatures in HE patients for diagnostic purposes. Importantly this was performed utilizing a non-invasive, portable bedside device and holds potential for future early HE diagnosis

Applications and Advances in Electronic-Nose Technologies

Electronic-nose devices have received considerable attention in the field of sensor technology during the past twenty years, largely due to the discovery of numerous applications derived from research in diverse fields of applied sciences. Recent applications of electronic nose technologies have come through advances in sensor design, material improvements, software innovations and progress in microcircuitry design and systems integration. The invention of many new e-nose sensor types and arrays, based on different detection principles and mechanisms, is closely correlated with the expansion of new applications. Electronic noses have provided a plethora of benefits to a variety of commercial industries, including the agricultural, biomedical, cosmetics, environmental, food, manufacturing, military, pharmaceutical, regulatory, and various scientific research fields. Advances have improved product attributes, uniformity, and consistency as a result of increases in quality control capabilities afforded by electronic-nose monitoring of all phases of industrial manufacturing processes. This paper is a review of the major electronic-nose technologies, developed since this specialized field was born and became prominent in the mid 1980s, and a summarization of some of the more important and useful applications that have been of greatest benefit to man

Bulk and Surface Acoustic Wave Sensor Arrays for Multi-Analyte Detection: A Review

Bulk acoustic wave (BAW) and surface acoustic wave (SAW) sensor devices have successfully been used in a wide variety of gas sensing, liquid sensing, and biosensing applications. Devices include BAW sensors using thickness shear modes and SAW sensors using Rayleigh waves or horizontally polarized shear waves (HPSWs). Analyte specificity and selectivity of the sensors are determined by the sensor coatings. If a group of analytes is to be detected or if only selective coatings (i.e., coatings responding to more than one analyte) are available, the use of multi-sensor arrays is advantageous, as the evaluation of the resulting signal patterns allows qualitative and quantitative characterization of the sample. Virtual sensor arrays utilize only one sensor but combine itwith enhanced signal evaluation methods or preceding sample separation, which results in similar results as obtained with multi-sensor arrays. Both array types have shown to be promising with regard to system integration and low costs. This review discusses principles and design considerations for acoustic multi-sensor and virtual sensor arrays and outlines the use of these arrays in multi-analyte detection applications, focusing mainly on developments of the past decade

Detection of liver dysfunction using a wearable electronic nose system based on semiconductor metal oxide sensors

The purpose of this exploratory study was to determine whether liver dysfunction can be generally classified using a wearable electronic nose based on semiconductor metal oxide (MOx) gas sensors, and whether the extent of this dysfunction can be quantified. MOx gas sensors are attractive because of their simplicity, high sensitivity, low cost, and stability. A total of 30 participants were enrolled, 10 of them being healthy controls, 10 with compensated cirrhosis, and 10 with decompensated cirrhosis. We used three sensor modules with a total of nine different MOx layers to detect reducible, easily oxidizable, and highly oxidizable gases. The complex data analysis in the time and non-linear dynamics domains is based on the extraction of 10 features from the sensor time series of the extracted breathing gas measurement cycles. The sensitivity, specificity, and accuracy for distinguishing compensated and decompensated cirrhosis patients from healthy controls was 1.00. Patients with compensated and decompensated cirrhosis could be separated with a sensitivity of 0.90 (correctly classified decompensated cirrhosis), a specificity of 1.00 (correctly classified compensated cirrhosis), and an accuracy of 0.95. Our wearable, non-invasive system provides a promising tool to detect liver dysfunctions on a functional basis. Therefore, it could provide valuable support in preoperative examinations or for initial diagnosis by the general practitioner, as it provides non-invasive, rapid, and cost-effective analysis results

Metal Oxide-based Gas Sensor Array for the VOCs Analysis in Complex Mixtures using Machine Learning

Detection of Volatile Organic Compounds (VOCs) from the breath is becoming a viable route for the early detection of diseases non-invasively. This paper presents a sensor array with three metal oxide electrodes that can use machine learning methods to identify four distinct VOCs in a mixture. The metal oxide sensor array was subjected to various VOC concentrations, including ethanol, acetone, toluene and chloroform. The dataset obtained from individual gases and their mixtures were analyzed using multiple machine learning algorithms, such as Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree, Linear Regression, Logistic Regression, Naive Bayes, Linear Discriminant Analysis, Artificial Neural Network, and Support Vector Machine. KNN and RF have shown more than 99% accuracy in classifying different varying chemicals in the gas mixtures. In regression analysis, KNN has delivered the best results with R2 value of more than 0.99 and LOD of 0.012, 0.015, 0.014 and 0.025 PPM for predicting the concentrations of varying chemicals Acetone, Toluene, Ethanol, and Chloroform, respectively in complex mixtures. Therefore, it is demonstrated that the array utilizing the provided algorithms can classify and predict the concentrations of the four gases simultaneously for disease diagnosis and treatment monitoring

Human Recognition from Video Sequences and Off-Angle Face Images Supported by Respiration Signatures

In this work, we study the problem of human identity recognition using human respiratory waveforms extracted from videos combined with component-based off- angle human facial images. Our proposed system is composed of (i) a physiology- based human clustering module and (ii) an identification module based on facial features (nose, mouth, etc.) fetched from face videos. In our proposed methodology we, first, manage to passively extract an important vital sign (breath), cluster human subjects into nostril motion vs. nostril non-motion groups, and, then, localize a set of facial features, before we apply feature extraction and matching.;Our novel human identity recognition system is very robust, since it is working well when dealing with breath signals and a combination of different facial components acquired in uncontrolled luminous conditions. This is achieved by using our proposed Motion Classification approach and Feature Clustering technique based on the breathing waveforms we produce. The contributions of this work are three-fold. First, we collected a set of different datasets where we tested our proposed approach. Specifically, we considered six different types of facial components and their combination, to generate face-based video datasets, which present two diverse data collection conditions, i.e. videos acquired in head fully frontal position (baseline) and head looking up pose. Second, we propose a new way of passively measuring human breath from face videos and show comparatively identical output against baseline breathing waveforms produced by an ADInstruments device. Third, we demonstrate good human recognition performance when using the pro- posed pre-processing procedure of Motion Classification and Feature Clustering, working on partial features of human faces.;Our method achieves increased identification rates across all datasets used, and it manages to obtain a significantly high identification rate (ranging from 96%-100% when using a single or a combination of facial features), yielding an average of 7% raise, when compared to the baseline scenario. To the best of our knowledge, this is the first time that a biometric system is composed of an important human vital sign (breath) that is fused with facial features is such an efficient manner

Every breath you take: The value of the electronic nose (e-nose) technology in the early detection of lung cancer.

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