Recent developments in minimally and truly non-invasive blood glucose monitoring techniques

The aim of this paper is to introduce the recent research and commercial developments in minimally and non invasive blood glucose monitoring technique

Analisis Kadar Aseton pada Gas Buang Pernafasan Penderita Diabetes Mellitus dan Normal Menggunakan Sensor MQ-135

Diabetes Mellitus merupakan salah satu penyakit utama yang menimbulkan ancaman terhadap kesehatan manusia dan telah menjadi epidemik secara universal. Kadar aseton pada penderita Diabetes Mellitus sangat tinggi jika dibandingkan dengan orang tanpa indikasi Diabetes Mellitus. Penelitian ini bertujuan untuk menganalis kadar aseton dalam gas buang pernafasan penderita Diabetes Mellitus dan normal menggunakan sensor MQ-135. Metode penelitian yang dilakukan meliputi perancangan alat, pembuatan alat, kalibrasi, uji coba alat dan analisis. Subjek penelitian terdiri dari 6 responden tanpa indikasi Diabetes Mellitus dan 6 responden dengan indikasi Diabetes Mellitus. Akurasi alat pada responden tanpa indikasi Diabetes Mellitus sebesar 88,01% dan pada responden dengan indikasi Diabetes Mellitus sebesar 95,35%. Alat ini layak digunakan sebagai alat deteksi kadar gula darah mandiri bagi pasien dengan indikasi Diabetes Mellitus. Sesuai dengan dengan ketentuan BPFK (Balai Pengamanan Fasilitas Kesehatan) bahwa batas ambang nilai error yang diizinkan yaitu 5%

Asthma Identification Using Gas Sensors and Support Vector Machine

The exhaled breath analysis is a procedure of measuring several types of gases that aim to identify various diseases in the human body. The purpose of this study is to analyze the gases contained in the exhaled breath in order to recognize healthy and asthma subjects with varying severity. An electronic nose consisting of seven gas sensors equipped with the Support Vector Machine classification method is used to analyze the gases to determine the patient's condition. Non-linear binary classification is used to identify healthy and asthma subjects, whereas the multiclass classification is applied to recognize the subjects of asthma with different severity. The result of this study showed that the system provided a low accuracy to distinguish the subjects of asthma with varying severity. This system can only differentiate between partially controlled and uncontrolled asthma subjects with good accuracy. However, this system can provide high sensitivity, specificity, and accuracy to distinguish between healthy and asthma subjects. The use of five gas sensors in the electronic nose system has the best accuracy in the classification results of 89.5%. The gases of carbon monoxide, nitric oxide, volatile organic compounds, hydrogen, and carbon dioxide contained in the exhaled breath are the dominant indications as biomarkers of asthma.The performance of electronic nose was highly dependent on the ability of sensor array to analyze gas type in the sample. Therefore, in further study we will employ the sensors having higher sensitivity to detect lower concentration of the marker gases

Non-Invasive Method of Human Exhaled Breath Analysis for Diabetes Detection Using Bidirectional Long-Short-Term Memory Algorithm

Volatile organic compounds (VOCs) have the potential to be used as biomarkers for pathophysiological and physical abnormalities associated with several disorders. A promising non-invasive metabolic monitoring method is the Analysis of VOCs in exhaled breath. It may also be used to monitor the development of certain diseases and their early detection. Diabetes is a metabolic disease and a complicated syndrome. The relationship between oxidative stress, inflammatory syndrome, hypertension, and diabetes is complicated. This study describes the creation of an Internet of Things (IoT) based breath analyzer to identify and track diseases using exhaled breath. Diabetic breath biomarkers and breath analysis are the main topics of discussion. A group of 25 diabetic patients and 15 non-diabetic individuals were tested using this system. Data is initially gathered using the wired module and the Cool Term software. The system is created for both wired and wireless devices. A deep learning algorithm analyses the disease characteristics after data collection. It clearly distinguishes between samples with diabetes and those without with 84% accuracy. This technology could detect a non-transmissible or transmissible disease early, preventing infection to others

Desarrollo de un sensor de glucosa no invasivo debido a sus propiedades dieléctricas

La diabetes es un trastorno metabólico a nivel global en la que la mayor parte de la población afectada debe regularse periódicamente a lo largo del día los niveles de glucosa en sangre. Con la intención de mejorar la calidad de vida de estas personas se ha intentado desarrollar un sensor de glucosa no invasivo. En este caso, la funcionalidad del sensor se centra en el estudio de la concentración de glucosa por medio de la relación entre la permitividad y la concentración de glucosa, utilizándose para este fin la tecnología de las frecuencias de microondas. En el presente trabajo se intentará ayudar a descubrir y caracterizar la relación existente entre la permitividad y la concentración de glucosa por medio del estudio de soluciones de células en medios con diferente concentración de glucosa, así como con cultivos celulares en monocapa. Para ello se utilizará un conector coaxial y dos resonadores distintos. Los datos obtenidos así como las conclusiones se detallarán a lo largo del presente trabajo.Diabetes is a global disease that requires the individual to measure periodically throughout the day their blood glucose levels. Non-invasive blood glucose monitoring systems have been made in order to improve the life quality of these people. In this case the functionality of the sensor is centered on the study of the glucose concentration on blood. For this reason it is important to characterize the relationship between permittivity and glucose concentration using the technology of microwave frequencies. In this paper we will try to discover and characterize the relationship between the permittivity and the glucose concentration through the study of cell solutions in mediums with different glucose concentration. This porpoise is going to be studied in monolayer cell cultures as well. A coaxial cable and two different resonators are used. The data and the conclusions obtained are detailed throughout this work

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

Deteksi Multilevel Diabetes secara NonInvasive dengan Analisis Napas Manusia menggunakan Breathalyzer

Diabetes merupakan penyakit metabolic yang banyak diderita. Namu sayangnya, hanya sedikit orang yang mengetahui penyakit metabolic ini, terutama di Indonesia dimana orang hanya melakukan pemeriksaan kesehatan saat mereka merasa sakit. Oleh karena itu, dalam penelitian ini kami mengusulkan sistem non-invasive yang mudah digunakan dan berbiaya rendah yang dapat membedakan orang sehat dan orang diabetes sehingga dapat dilakukan pencegahan dini. Ada tujuh tahap utama untuk membangun sistem ini, pembuatan perangkat keras e-Nose menggunakan sensor mikrokontroler dan gas, akuisisi data ground-truth untuk set pelatihan, pemrosesan sinyal menggunakan Discrete Wavelet Transform (DWT) dan normalisasi Z-score, fitur statistik Ekstraksi, pemilihan fitur untuk optimasi, klasifikasi, dan evaluasi kinerja e-Nose. Hasil percobaan menunjukkan bahwa sistem ini dapat membedakan pasien sehat dan diabetes dengan kinerja yang menjanjikan (95,0% akurasi, ketepatan 91,30% diabetes, ketepatan 94,12% sehat dan 0,898 kappa statistik) dengan menggunakan classifier k-NN. ================================================================= Diabetes is a disease that many people suffer. However, unfortunately only a few people that aware of this metabolic disease especially in Indonesia where people only do health check when they are feeling sick. Therefore, in this research we propose non-invasive, easy to use, and low-cost system that can distinguish healthy or diabetes people so they can have early preventive action. There are seven main stages to build this system, the making of e-Nose hardware using microcontroller and gas sensors, ground-truth data acquisitions for the training set, signal processing using Discrete Wavelet Transform (DWT) and Z-score normalization, statistical features extraction, feature selection for optimization, classification, and e-Nose performance evaluation. The experiment results show that this system can distinguish healthy and diabetes patients with promi sing performance (95.0% of accuracy, 91.30% precision of diabetes, 94.12% precision of healthy and 0.898 kappa statistic’s value) using k-NN classifier