Identification of hypoglycemic states for patients with T1DM using various parameters derived from EEG signals

For patients with Type 1 Diabetes Mellitus (T1DM), hypoglycemia is a very common but dangerous complication which can lead to unconsciousness, coma and even death. The variety of hypoglycemia symptoms is originated from the inadequate supply of glucose to the brain. In this study, we explore the connection between hypoglycemic episodes and the electrical activity of neurons within the brain or electroencephalogram (EEG) signals. By analyzing EEG signals from a clinical study of five children with T1DM, associated with hypoglycemia at night, we find that some EEG parameters change significantly under hypoglycemia condition. Based on these parameters, a method of detecting hypoglycemic episodes using EEG signals with a feed-forward multi-layer neural network is proposed. In our application, the classification results are 72% sensitivity and 55% specificity when the EEG signals are acquired from 2 electrodes C3 and O2. Furthermore, signals from different channels are also analyzed to observe the contributions of each channel to the performance of hypoglycemia classification. © 2011 IEEE

EDMON - Electronic Disease Surveillance and Monitoring Network: A Personalized Health Model-based Digital Infectious Disease Detection Mechanism using Self-Recorded Data from People with Type 1 Diabetes

Through time, we as a society have been tested with infectious disease outbreaks of different magnitude, which often pose major public health challenges. To mitigate the challenges, research endeavors have been focused on early detection mechanisms through identifying potential data sources, mode of data collection and transmission, case and outbreak detection methods. Driven by the ubiquitous nature of smartphones and wearables, the current endeavor is targeted towards individualizing the surveillance effort through a personalized health model, where the case detection is realized by exploiting self-collected physiological data from wearables and smartphones. This dissertation aims to demonstrate the concept of a personalized health model as a case detector for outbreak detection by utilizing self-recorded data from people with type 1 diabetes. The results have shown that infection onset triggers substantial deviations, i.e. prolonged hyperglycemia regardless of higher insulin injections and fewer carbohydrate consumptions. Per the findings, key parameters such as blood glucose level, insulin, carbohydrate, and insulin-to-carbohydrate ratio are found to carry high discriminative power. A personalized health model devised based on a one-class classifier and unsupervised method using selected parameters achieved promising detection performance. Experimental results show the superior performance of the one-class classifier and, models such as one-class support vector machine, k-nearest neighbor and, k-means achieved better performance. Further, the result also revealed the effect of input parameters, data granularity, and sample sizes on model performances. The presented results have practical significance for understanding the effect of infection episodes amongst people with type 1 diabetes, and the potential of a personalized health model in outbreak detection settings. The added benefit of the personalized health model concept introduced in this dissertation lies in its usefulness beyond the surveillance purpose, i.e. to devise decision support tools and learning platforms for the patient to manage infection-induced crises

Quantitative analysis of hypoglycemia-induced EEG alterations in type 1 diabetes

The main risk for patients affected by type 1 diabetes (T1D) is to fall in hypoglycemia, an event which leads to both short and long-terms automatic failure and can be life-threatening especially when occurs at night without subject awareness. Moreover, T1D patients can develop asymptomatic hypoglycemia, reducing the prompt response of the counterregulatory system triggered by the fall in blood glucose. Avoiding hypoglycemia is important in children and adolescents because hypoglycemia episodes may have clinically relevant effects on cognition. Also in adults, cognitive tests assessed that hypoglycemia results in altered cerebral activity, most likely due to the complete dependence of the brain for glucose supply. The first organ influenced by this fall of glucose in the blood is the brain. Indeed, a lot of studies proved the mirroring of cognitive dysfunction due to hypoglycemia in the spectral power of the electroencephalogram (EEG) signal. In particular, the increase of the power in low frequency EEG bands is a well-known effect during hypoglycemia that seems more pronounced in the EEG recording in the posterior areas of the brain. Pilot studies about the real-time processing of the EEG signal to detect hypoglycemia have indicated that it might be possible to alert the patients by means of EEG analysis. The main advantages in exploiting EEG analysis is that the blood glucose threshold to enter in hypoglycemia has large inter-subjects variations, on the contrary the EEG onset in general occurs before the state of hypoglycemia is critical, i.e., the brain starts to experience neuroglycopoenia and its functions completely fail. The main aim of this work is to broaden out the quantitative analysis on the altered EEG activity due to hypoglycemia in T1D patients to identify potential margins of improvement in EEG processing and further features sensitive to hypoglycemia. In particular, the analyses are extended to different domains, i.e., time and frequency domains, to deepen the knowledge on the effects of hypoglycemia in the brain. So far, studies in the literature have mainly evaluated these changes only on a single EEG channel level on the frequency domain, but limited information is available on the hypoglycemia influence on brain network dynamics and on connection between different brain areas. To do so, this dissertation is structured in 7 chapters, briefly presented below. Chapter 1 will start with a brief overview about the impact of T1D and its main effects on daily life. Moreover, the main consequences of hypoglycemia in human brain will be described by reporting the main findings in the literature. Chapter 2 will present the database where EEG data and blood glucose samples were collected in parallel for about 8 h in 31 T1D hospitalized patients during an hyperinsulinemic - hypoglycemic clamp experiment. Chapter 3 will address on the main effects of hypoglycemia in the frequency domain. After testing the well-known changes in the spectral power of the EEG signal during hypoglycemia, a multivariate analysis based on the concept of Information Partial Directed Coherence will be presented. In particular, we will confirm the general slowing in the frequency domain and we will show how hypoglycemia affects the EEG functional connectivity. Chapter 4 will consider the effects of hypoglycemia on EEG complexity. Fractal dimension features, describing both amplitude and frequency properties, will be computed and compared with the results based on Sample Entropy. We will reveal a decrease of EEG signal complexity in the hypoglycemic condition. Chapter 5 will focus on the consequences of hypoglycemia in the so-called microstates or "athoms of thought". We will hypothesize that the changes in the frequency domain and the decrease of the EEG signal complexity in hypoglycemia have in common the same resting EEG electric potential amplitude map. Chapter 6 will describe how hypoglycemia influences the results of cognitive tests, and the relationship between the drop in the tests performance and the EEG quantitative measures presented in the previous chapters. We will find a direct correlation among the changes in the power spectra, the cognitive tests performance and the changes of one resting EEG electric potential amplitude map. Eventually, Chapter 7 will close the dissertation by interpreting the ensemble of the results from both the medical and engineering point of view, and presenting the possible future developments of this work

Le diabète de type 1 ou 2 pré-grossesse altère-t-il l'acide docosahexaénoïque et l'acide arachidonique dans le sang de cordon et le neuro-développement du nouveau-né?

La prévalence des grossesses avec diabète augmente sans cesse et cela peut engendrer des conséquences pour l’enfant, tel que des scores cognitifs plus faibles. L'acide docosahexaénoïque (DHA) et l'acide arachidonique (AA) sont importants pour le développement cérébral du foetus. Ces acides gras doivent provenir de l’alimentation puisque l’humain ne peut les fabriquer et leur transfert de la mère au foetus dans les grossesses avec diabète semble être modifié. D’ailleurs, dans les grossesses compliquées par le diabète gestationnel (GDM), le pourcentage relatif de DHA est plus élevé dans le plasma des mères mais plus faible dans le sang de cordon par rapport aux contrôles sans diabète. Nous pensons que dans chez les mères avec un diabète diagnostiqué avant la grossesse, le transfert de DHA vers le foetus sera moins efficace puisque ces femmes ont des hypo- et hyperglycémies plus fréquentes. Par conséquent, notre hypothèse est que, pendant la grossesse, les femmes avec un diabète de type 1 et de type 2 (DT1; DT2) diagnostiqué avant la grosses auront un transfert plus faible de DHA et d'AA de la mère au foetus, ce qui aura pour conséquence un neurodéveloppement plus faible de leur nouveau-né. Sept femmes enceintes avec un DT1 ou un DT2 préexistant et 26 sans diabète ont été recrutées. Le profil en acides gras des lipides totaux du plasma maternel, du sérum et du sang du cordon ombilical, et du placenta maternel et foetal a été effectué. Une électroencéphalographie a été effectué chez le nouveau-né 24-48 heures après la naissance et 48 semaines post-aménorrhée. Nos résultats montrent que la concentration d'AA, mais non de DHA, était significativement plus faible dans les lipides totaux du sérum du cordon des participantes diabétiques comparativement aux nondiabétiques mais que le niveaux d’AA dans le plasma maternel n’était pas différent entre les groupes. Ce résultat suggère un potentiel dysfonctionnement du transfert de l’AA dans le placenta mais nos résultats n’ont pas montré d'accumulation d'AA ou de DHA dans le placenta maternel ou foetal. Malgré ce transfert plus faible de l'AA, le neurodéveloppement des nouveau-nés tel qu’évalué par électroencéphalographie était similaire dans les deux groupes. Bien que les résultats démontrent un transfert plus faible d'AA de la mère au foetus dans les grossesses avec diabète, ils n'expliquent pas le mécanisme. L'étude des niveaux de transporteurs d'acides gras placentaires et des niveaux d'AA dans différents classes lipidiques permettrait de mieux comprendre ce mécanisme.Abstract : The prevalence of diabetic pregnancies has been increasing over the last several decades. Diabetic pregnancies are associated with long-term negative outcomes for the offspring, including lower cognitive scores. Several groups have also investigated the transfer of docosahexaenoic acid (DHA) and arachidonic acid (AA) from mother to fetus in diabetic pregnancies, since these fatty acids are important for fetal brain development. These fatty acids must be obtained through the diet since humans cannot make them. Previous studies have shown that in pregnancies complicated by gestational diabetes mellitus (GDM), relative percentage of DHA is higher in the plasma of GDM mothers but lower in the cord blood of GDM mothers compared to controls. We believe that in pre-existing diabetes, the transfer of DHA and AA to the fetus will be less efficient given the greater frequency of hypo- and hyperglycemia in these women. Therefore, we hypothesize that pre-existing type 1 and type 2 diabetes (T1D and T2D) during pregnancy lowers the transfer of DHA and AA from mother to fetus which results in lower neurodevelopment of the neonate. Seven pregnant women with pre-existing T1D or T2D and 26 without diabetes were recruited. The fatty acid profile of the total lipids from the maternal plasma, umbilical cord serum and whole blood, and the maternal and fetal placenta were performed. An electroencephalography in the neonate was also performed 24-48 hours after birth and 48 weeks post-amenorrhea. AA concentration, but not DHA, was significantly lower in the umbilical cord serum total lipids of the diabetic group compared to the non-diabetic group. However, AA levels in the maternal plasma were similar between diabetic and non-diabetic groups. This indicates that there may be a dysfunction at the level of the placenta which results in a lower transfer of AA to the fetus in diabetic pregnancies. However, there was no accumulation of AA or DHA in the maternal or fetal sides placenta. Despite the lower transfer of AA from mother to fetus in the diabetic group, there was no evidence of impaired neurodevelopment in the EEGs of the neonates from the diabetic group at either time point. While the results demonstrate lower transfer of AA from mother to fetus in diabetic pregnancies, they do not explain the mechanism. The investigation of placental fatty acid transporter levels and AA levels in different lipid pools would aid to further clarify this mechanism

Applications of the Internet of Medical Things to Type 1 Diabetes Mellitus

Type 1 Diabetes Mellitus (DM1) is a condition of the metabolism typified by persistent hyperglycemia as a result of insufficient pancreatic insulin synthesis. This requires patients to be aware of their blood glucose level oscillations every day to deduce a pattern and anticipate future glycemia, and hence, decide the amount of insulin that must be exogenously injected to maintain glycemia within the target range. This approach often suffers from a relatively high imprecision, which can be dangerous. Nevertheless, current developments in Information and Communication Technologies (ICT) and innovative sensors for biological signals that might enable a continuous, complete assessment of the patient’s health provide a fresh viewpoint on treating DM1. With this, we observe that current biomonitoring devices and Continuous Glucose Monitoring (CGM) units can easily obtain data that allow us to know at all times the state of glycemia and other variables that influence its oscillations. A complete review has been made of the variables that influence glycemia in a T1DM patient and that can be measured by the above means. The communications systems necessary to transfer the information collected to a more powerful computational environment, which can adequately handle the amounts of data collected, have also been described. From this point, intelligent data analysis extracts knowledge from the data and allows predictions to be made in order to anticipate risk situations. With all of the above, it is necessary to build a holistic proposal that allows the complete and smart management of T1DM. This approach evaluates a potential shortage of such suggestions and the obstacles that future intelligent IoMT-DM1 management systems must surmount. Lastly, we provide an outline of a comprehensive IoMT-based proposal for DM1 management that aims to address the limits of prior studies while also using the disruptive technologies highlighted beforePartial funding for open access charge: Universidad de Málag

Hypoglycemia

Glucose is an essential metabolic substrate of all mammalian cells being the major carbohydrate presented to the cell for energy production and also many other anabolic requirements. Hypoglycemia is a disorder where the glucose serum concentration is usually low. The organism usually keeps the glucose serum concentration in a range of 70 to 110 mL/dL of blood. In hypoglycemia the glucose concentration normally remains lower than 50 mL/dL of blood. This book provides an abundance of information for all who need them in order to help many people worldwide

The new technique for accurate estimation of the spinal cord circuitry:recording reflex responses of large motor unit populations

We propose and validate a non-invasive method that enables accurate detection of the discharge times of a relatively large number of motor units during excitatory and inhibitory reflex stimulations. HDsEMG and intramuscular EMG (iEMG) were recorded from the tibialis anterior muscle during ankle dorsiflexions performed at 5%, 10%, and 20% of the maximum voluntary contraction (MVC) force, in 9 healthy subjects. The tibial nerve (inhibitory reflex) and the peroneal nerve (excitatory reflex) were stimulated with constant current stimuli. In total, 416 motor units were identified from the automatic decomposition of the HDsEMG. The iEMG was decomposed using a state-of-the-art decomposition tool and provided 84 motor units (average of two recording sites). The reflex responses of the detected motor units were analyzed using the peri-stimulus time histogram (PSTH) and the peri-stimulus frequencygram (PSF). The reflex responses of the common motor units identified concurrently from the HDsEMG and the iEMG signals showed an average disagreement (the difference between number of observed spikes in each bin relative to the mean) of 8.2±2.2% (5% MVC), 6.8±1.0% (10% MVC), and 7.5±2.2% (20% MVC), for reflex inhibition, and 6.5±4.1%, 12.0±1.8%, 13.9±2.4%, for reflex excitation. There was no significant difference between the characteristics of the reflex responses, such as latency, amplitude and duration, for the motor units identified by both techniques. Finally, reflex responses could be identified at higher force (four of the nine subjects performed contraction up to 50% MVC) using HDsEMG but not iEMG, because of the difficulty in decomposing the iEMG at high forces. In conclusion, single motor unit reflex responses can be estimated accurately and non-invasively in relatively large populations of motor units using HDsEMG. This non-invasive approach may enable a more thorough investigation of the synaptic input distribution on active motor units at various force levels