Noninvasive blood glucose monitoring system based on near-infrared method

Diabetes is considered one of the life-threatening diseases in the world which need continuous monitoring to avoid the complication of diabetes. There is a need to develop a non-invasive monitoring system that avoids the risk of infection problems and pain caused by invasive monitoring techniques. This paper presents a method for developing a noninvasive technique to predict the blood glucose concentration (BCG) based on the Near-infrared (NIR) light sensor. A prototype is developed using a finger sensor based on LED of 940 nm wavelength to collect photoplethysmography (PPG) signal which is variable depending on the glucose concentration variance, a module circuit to preprocess PPG signals is realized, which includes an amplifier and analog filter circuits, an Arduino UNO is used to analog-to-digital conversion. A digital Butterworth filterer is used to remove PPG signal trends, then detect the PPG data peaks to determine the relationship between the PPG signal and (BCG) and use it as input parameters to build the calibration model based on linear regression. Experiments show that the Root Mean Squares Error (RMSE) of the prediction is between 8.264mg/dL and 13.166 mg/dL, the average of RMSE is about 10.44mg/dL with a correlation coefficient (R^2) of 0.839, it is observed that the prediction of glucose concentration is in the clinically acceptable region of the standard Clark Error Grid (CEG)

Applications of machine learning in spectroscopy

The way to analyze data in spectroscopy has changed substantially. At the same time, data science has evolved to the point where spectroscopy can find space to be housed, adapted and be functional. The integration of the two sciences has introduced a knowledge gap between data scientists who know about advanced machine learning techniques and spectroscopists who have a solid background in chemometrics. To reach a symbiosis, the knowledge gap requires bridging. This review article focuses on introducing data science subjects to non-specialist spectroscopists, or those unfamiliar with the subject. The article will explain concepts that are covered in machine learning, such as supervised learning, unsupervised learning, deep learning, and most importantly, the difference between machine learning and artificial intelligence. This article also includes examples of published spectroscopy research, in which some of the concepts explained here are applied. Machine learning together with spectroscopy can provide a useful, fast, and efficient tool to analyze samples of interest both for industrial and research purposes. © 2020 Taylor & Francis Group, LLC

Projeto não invasivo de medição de glicose baseado em espectroscopia de infravermelho próximo

O Diabetes Mellitus, uma doença metabólica crônica, apresenta-se como um desafio global de saúde, com projeções de 642 milhões de casos até 2040. Atualmente, encontra-se entre as dez principais causas de morte em países de renda média-baixa, demandando monitoramento diário. A falta de técnicas não invasivas para medir a glicose torna esse processo repetitivo, doloroso e suscetível a infecções. Portanto, há uma urgência na pesquisa e desenvolvimento de tecnologias para auxiliar no tratamento e controle dos índices glicêmicos. A espectroscopia de infravermelho próximo, embora uma tecnologia previamente limi- tada pelo alto custo, agora está se popularizando devido aos avanços tecnológicos. Este projeto utiliza essa técnica para criar um protótipo destinado a medir diferentes concen- trações de glicose, tanto in vitro quanto in vivo. Os resultados deste estudo revelam que diferentes comprimentos de onda (625 nm, 950 nm, 1450 nm e 1720 nm) interagem de maneiras distintas com a glicose. Essas inte- rações resultam em notáveis diferenças diante das concentrações observadas nas análises realizadas, sendo essas concentrações de 50 até 2000 mg/dL de glicose. Em resumo, este estudo contribui para o avanço da pesquisa sobre diabetes. Os métodos utilizados para testes e análises demonstraram eficácia, embora seja necessária uma melhoria técnica para atender aos requisitos clínicos na medição não invasiva de glicose.Diabetes Mellitus, a chronic metabolic disease, poses a global health challenge with pro- jections of 642 million cases by 2040. Currently among the top ten causes of death in low- to middle-income countries, it necessitates daily monitoring. The absence of non- invasive glucose measurement techniques makes the process repetitive, painful, and prone to infections, urging research and technology development for glycemic control. Near-infrared spectroscopy, previously constrained by high costs, is now gaining po- pularity due to technological advancements. This project utilizes this technique to create a prototype for measuring different concentrations of glucose, both in vitro and in vivo. The results of this study reveal that different wavelengths (625 nm, 950 nm, 1450 nm, and 1720 nm) interact distinctively with glucose. These interactions lead to notable differences in the observed concentrations in the conducted analyses, ranging from 50 to 2000 mg/dL of glucose. In summary, this study contributes to the advancement of diabetes research. The methods used for testing and analyses have demonstrated efficacy, although technical im- provement is needed to meet clinical requirements for non-invasive glucose measurement

Leveraging Artificial Intelligence to Improve EEG-fNIRS Data Analysis

La spectroscopie proche infrarouge fonctionnelle (fNIRS) est apparue comme une technique de neuroimagerie qui permet une surveillance non invasive et à long terme de l'hémodynamique corticale. Les technologies de neuroimagerie multimodale en milieu clinique permettent d'étudier les maladies neurologiques aiguës et chroniques. Dans ce travail, nous nous concentrons sur l'épilepsie - un trouble chronique du système nerveux central affectant près de 50 millions de personnes dans le monde entier prédisposant les individus affectés à des crises récurrentes. Les crises sont des aberrations transitoires de l'activité électrique du cerveau qui conduisent à des symptômes physiques perturbateurs tels que des changements aigus ou chroniques des compétences cognitives, des hallucinations sensorielles ou des convulsions de tout le corps. Environ un tiers des patients épileptiques sont récalcitrants au traitement pharmacologique et ces crises intraitables présentent un risque grave de blessure et diminuent la qualité de vie globale. Dans ce travail, nous étudions 1. l'utilité des informations hémodynamiques dérivées des signaux fNIRS dans une tâche de détection des crises et les avantages qu'elles procurent dans un environnement multimodal par rapport aux signaux électroencéphalographiques (EEG) seuls, et 2. la capacité des signaux neuronaux, dérivé de l'EEG, pour prédire l'hémodynamique dans le cerveau afin de mieux comprendre le cerveau épileptique. Sur la base de données rétrospectives EEG-fNIRS recueillies auprès de 40 patients épileptiques et utilisant de nouveaux modèles d'apprentissage en profondeur, la première étude de cette thèse suggère que les signaux fNIRS offrent une sensibilité et une spécificité accrues pour la détection des crises par rapport à l'EEG seul. La validation du modèle a été effectuée à l'aide de l'ensemble de données CHBMIT open source documenté et bien référencé avant d'utiliser notre ensemble de données EEG-fNIRS multimodal interne. Les résultats de cette étude ont démontré que fNIRS améliore la détection des crises par rapport à l'EEG seul et ont motivé les expériences ultérieures qui ont déterminé la capacité prédictive d'un modèle d'apprentissage approfondi développé en interne pour décoder les signaux d'état de repos hémodynamique à partir du spectre complet et d'une bande de fréquences neuronale codée spécifique signaux d'état de repos (signaux sans crise). Ces résultats suggèrent qu'un autoencodeur multimodal peut apprendre des relations multimodales pour prédire les signaux d'état de repos. Les résultats suggèrent en outre que des gammes de fréquences EEG plus élevées prédisent l'hémodynamique avec une erreur de reconstruction plus faible par rapport aux gammes de fréquences EEG plus basses. De plus, les connexions fonctionnelles montrent des modèles spatiaux similaires entre l'état de repos expérimental et les prédictions fNIRS du modèle. Cela démontre pour la première fois que l'auto-encodage intermodal à partir de signaux neuronaux peut prédire l'hémodynamique cérébrale dans une certaine mesure. Les résultats de cette thèse avancent le potentiel de l'utilisation d'EEG-fNIRS pour des tâches cliniques pratiques (détection des crises, prédiction hémodynamique) ainsi que l'examen des relations fondamentales présentes dans le cerveau à l'aide de modèles d'apprentissage profond. S'il y a une augmentation du nombre d'ensembles de données disponibles à l'avenir, ces modèles pourraient être en mesure de généraliser les prédictions qui pourraient éventuellement conduire à la technologie EEG-fNIRS à être utilisée régulièrement comme un outil clinique viable dans une grande variété de troubles neuropathologiques.----------ABSTRACT Functional near-infrared spectroscopy (fNIRS) has emerged as a neuroimaging technique that allows for non-invasive and long-term monitoring of cortical hemodynamics. Multimodal neuroimaging technologies in clinical settings allow for the investigation of acute and chronic neurological diseases. In this work, we focus on epilepsy—a chronic disorder of the central nervous system affecting almost 50 million people world-wide predisposing affected individuals to recurrent seizures. Seizures are transient aberrations in the brain's electrical activity that lead to disruptive physical symptoms such as acute or chronic changes in cognitive skills, sensory hallucinations, or whole-body convulsions. Approximately a third of epileptic patients are recalcitrant to pharmacological treatment and these intractable seizures pose a serious risk for injury and decrease overall quality of life. In this work, we study 1) the utility of hemodynamic information derived from fNIRS signals in a seizure detection task and the benefit they provide in a multimodal setting as compared to electroencephalographic (EEG) signals alone, and 2) the ability of neural signals, derived from EEG, to predict hemodynamics in the brain in an effort to better understand the epileptic brain. Based on retrospective EEG-fNIRS data collected from 40 epileptic patients and utilizing novel deep learning models, the first study in this thesis suggests that fNIRS signals offer increased sensitivity and specificity metrics for seizure detection when compared to EEG alone. Model validation was performed using the documented open source and well referenced CHBMIT dataset before using our in-house multimodal EEG-fNIRS dataset. The results from this study demonstrated that fNIRS improves seizure detection as compared to EEG alone and motivated the subsequent experiments which determined the predictive capacity of an in-house developed deep learning model to decode hemodynamic resting state signals from full spectrum and specific frequency band encoded neural resting state signals (seizure free signals). These results suggest that a multimodal autoencoder can learn multimodal relations to predict resting state signals. Findings further suggested that higher EEG frequency ranges predict hemodynamics with lower reconstruction error in comparison to lower EEG frequency ranges. Furthermore, functional connections show similar spatial patterns between experimental resting state and model fNIRS predictions. This demonstrates for the first time that intermodal autoencoding from neural signals can predict cerebral hemodynamics to a certain extent. The results of this thesis advance the potential of using EEG-fNIRS for practical clinical tasks (seizure detection, hemodynamic prediction) as well as examining fundamental relationships present in the brain using deep learning models. If there is an increase in the number of datasets available in the future, these models may be able to generalize predictions which would possibly lead to EEG-fNIRS technology to be routinely used as a viable clinical tool in a wide variety of neuropathological disorders

Investigating the biomechanics and biochemistry underlying MRI measures of neuronal function

This thesis investigates the biophysical mechanisms underlying functional magnetic resonance imaging (fMRI) measures of brain activity. Diffusion-weighted fMRI (DWfMRI) has been suggested as an alternative to the established Blood Oxygenation Level Dependent (BOLD) method. It is speculated to be sensitive to transient microstructural changes within active brain tissue, which could provide a more direct measure of neuronal activity than techniques relying on attendant haemodynamic changes. DWfMRI has yet to become widely accepted however, as the mechanism driving the observed signal is not well understood. Here, experimental and theoretical investigations of the fMRI signal are presented. As part of this work, a functional MRI study was undertaken to compare BOLD and DWfMRI responses to stimulated brain activity in human volunteers. The effect of different experimental protocols were explored, with an emphasis on stimulus design. Analysis methods and their potential impact on interpretation of the response are explored. Neuronal activation is accompanied by heamodynamic changes detectable with Optical Imaging Spectroscopy. Additionally, there is a growing base of evidence showing microstructural changes in excited neuronal tissue. This tortuosity change might be observable through the use of Spatial Frequency Domain Imaging (SFDI). These properties can be observed in the animal model and compared with fMRI to aid interpretation. The following work presents the development of in-vivo optical imaging techniques for the measurement of tissue optical property changes during brain activity. This includes theoretical explorations of the analysis pipeline, and of the potential limitations of these techniques and their sensitivity. A Monte Carlo simulation of light transport through tissue was written to provide calibration data for the optical imaging methods. The simulation was used to explore the impact of tissue parameters on the optical results and inform interpretation. The simulation was extended to explore tissue absorption in the context of biophotomodulation

Dynamic Thermal Imaging for Intraoperative Monitoring of Neuronal Activity and Cortical Perfusion

Neurosurgery is a demanding medical discipline that requires a complex interplay of several neuroimaging techniques. This allows structural as well as functional information to be recovered and then visualized to the surgeon. In the case of tumor resections this approach allows more fine-grained differentiation of healthy and pathological tissue which positively influences the postoperative outcome as well as the patient's quality of life. In this work, we will discuss several approaches to establish thermal imaging as a novel neuroimaging technique to primarily visualize neural activity and perfusion state in case of ischaemic stroke. Both applications require novel methods for data-preprocessing, visualization, pattern recognition as well as regression analysis of intraoperative thermal imaging. Online multimodal integration of preoperative and intraoperative data is accomplished by a 2D-3D image registration and image fusion framework with an average accuracy of 2.46 mm. In navigated surgeries, the proposed framework generally provides all necessary tools to project intraoperative 2D imaging data onto preoperative 3D volumetric datasets like 3D MR or CT imaging. Additionally, a fast machine learning framework for the recognition of cortical NaCl rinsings will be discussed throughout this thesis. Hereby, the standardized quantification of tissue perfusion by means of an approximated heating model can be achieved. Classifying the parameters of these models yields a map of connected areas, for which we have shown that these areas correlate with the demarcation caused by an ischaemic stroke segmented in postoperative CT datasets. Finally, a semiparametric regression model has been developed for intraoperative neural activity monitoring of the somatosensory cortex by somatosensory evoked potentials. These results were correlated with neural activity of optical imaging. We found that thermal imaging yields comparable results, yet doesn't share the limitations of optical imaging. In this thesis we would like to emphasize that thermal imaging depicts a novel and valid tool for both intraoperative functional and structural neuroimaging

Analysis and interpretation of electrocardiogram signals for the detection of hypoglycaemia.

Diabetes is a complication of metabolism where the glucose control system of the human body is impaired and cannot preserve the blood glucose levels in the normal range. This research investigated the relationship between abnormally low glucose levels (hypoglycaemia) and cardiac function in human subjects with Type 1 diabetes. The aim of the research was to detect the onset of spontaneous nocturnal hypoglycaemia indirectly through analysis of the subject's Electrocardiogram (ECG). The research hypothesis follows from previous studies, that suggested changes in ECG morphology, in particular prolongation of the QT interval and flattening of the T-wave, during hypoglycaemia.The research methodology involved ECG feature extraction and classification of extracted features into euglycaemic (normal glucose levels) and hypoglycaemic categories. A number of time-domain ECG features were evaluated and a few ECG annotation algorithms were investigated for detection of onsets, peaks and offsets of the ECG components. Autoregressive (AR) modelling was also employed as a means of describing and characterising post-QRS ECG segments. ECG segment classification was carried out using Multi-layer Perceptron (MLP) neural networks. Statistical classifiers were also employed namely, Linear Discriminant Analysis (LDA) and the k-Nearest Neighbour (kNN).This research proposed a new methodology for detection of spontaneous nocturnal hypoglycaemia by combining time-domain characterisation and classification of the post-QRS ECG segment. Two novel ECG features were introduced to characterise T-wave morphology. MLPs achieved better classification of ECG feature vectors compared to LDA. Also ECG representation by AR coefficients was marginally superior to individual ECG features, according to classification performance by LDA. Finally a Knowledge-Based System (KBS) was designed for ECG monitoring during the night. It was developed and tested onoffline data in a m anner that simulated an online monitoring scenario. The system was able to detect ECG abnormalities related to spontaneous nocturnal hypoglycaemia and to raise an alarm if necessary. In its optimal configuration, the system correctly monitored 30 out of the 32 recorded nights (originating from 19 patients) while there were 2 false alarms. This performance corresponds to accuracy, sensitivity and specificity of 93.75%, 100% and 91.30% respectively.The main contribution to knowledge from this research was successful detection of the onset of spontaneous nocturnal hypoglycaemia indirectly, using solely ECG information. This result supports the hypothesis stating that spontaneous hypoglycaemia affects the cardiac function and is manifested on the ECG. A. detailed analysis of the ECG signal for the detection of hypoglycaemia was carried out in the thesis. ECG features were extracted and assessed as predictors of the clinical condition. A number of approaches for ECG representation and classification (MLP, kNN, LDA) were examined and compared. Moreover, a KBS capable of achieving satisfactory monitoring performance on offline data from diabetic patients was designed. It was found that ECG changes in response to hypoglycaemia were short-time transients and incorporation of temporal information in the classification system caused significant improvement in performance. Successful continuation of this work may lead to a hypoglycaemia-detection system for the bedside