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

Hypoglycemia detection in patients with type 1 diabetes using EEG signals

The main risk for patients with type 1 diabetes (T1D) is to fall into hypoglycemia. We have extended the quantitative detection of hypoglycemia from the altered EEG signal in T1D patients by analyzing all EEG channel data through different measures of signal complexity such as the fractal domain and entropy indices. Finally, they were classified through a neural network in order to detect hypoglycemia with a high percentage of precision using the results obtained from the complexity analysis

Quantitative Assessment of Hypoglycaemia-Induced EEG changes in Type 1 Diabetes Subjects

Scopo di questa tesi è valutare quantitativamente come l’ipoglicemia influenza le caratteristiche dell’EEG, analizzando 19 tracciati EEG e i corrispettivi dati BG registrati in parallelo in soggetti T1D durante un esperimento di ipoglicemia indotta.Tre periodi di un’ora, riferiti a iper eu e ipo-glicemia, selezionati dai dati BG, sono elaborati con metodi basati sull’analisi spettrale per scoprire se specifiche caratteristiche dell'EEG cambiano durante il passaggio da eu a ipoglicemiaopenEmbargo per motivi di segretezza e/o di proprietà dei risultati e informazioni di enti esterni o aziende private che hanno partecipato alla realizzazione del lavoro di ricerca relativo alla tes

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

Predictive Internet of Things Based Detection Model of Comatose Patient using Deep Learning

The needs and demands of the healthcare sector are increasing exponentially. Also, there has been a rapid development in diverse technologies in totality. Hence varied advancements in different technologies like Internet of Things (IoT) and Deep Learning are being utilised and play a vital role in healthcare sector. In health care domain, specifically, there is also increasing need to find the possibility of patient going into coma. This is because if it is found that the patient is going into coma, preventive steps could be initiated helping patient and this could possibly save the life of the patient. The proposed work in this paper is in this direction whereby the advancement in technology is utilised to build a predictive model towards forecasting the chances of a patient going into coma state. The proposed system initially consists of different medical devices like sensors which take inputs from the patient and helps aid to monitor the condition of the patient. The proposed system consists of varied sensing devices which will help to record patient’s details such as blood pressure (B.P.), pulse rate, heart rate, brain signal and continuous monitoring the motion of coma patient. The various vital parameters from the patient are taken in continuously and displayed across a graphical display unit. Further as and when even if one vital parameter exceeds certain thresholds, the probability that patient will go into coma increases. Immediately an alert is given in. Further, all such records where there are chances that patient goes into coma state are stored in cloud. Subsequently, based on the data retrieved from the cloud a predictive model using Convolutional Neural Network (CNN) is built to forecast the status of the coma patient as an output for any set of health-related parameters of the patient. The effectiveness of the built predictive model is evaluated in terms of performance metrics such as accuracy, precision and recall. The built forecasting model displays high accuracy up to 98%. Such a system will greatly benefit health sector and coma patients and enable build futuristic and superior predictive and preventive model helping in reducing cases of patient going into coma state

Protocol for the Birth Asphyxia in African Newborns (Baby BRAiN) Study: a Neonatal Encephalopathy Feasibility Cohort Study

BACKGROUND: Neonatal encephalopathy (NE) is a leading cause of child mortality worldwide and contributes substantially to stillbirths and long-term disability. Ninety-nine percent of deaths from NE occur in low-and-middle-income countries (LMICs). Whilst therapeutic hypothermia significantly improves outcomes in high-income countries, its safety and effectiveness in diverse LMIC contexts remains debated. Important differences in the aetiology, nature and timing of neonatal brain injury likely influence the effectiveness of postnatal interventions, including therapeutic hypothermia. METHODS: This is a prospective pilot feasibility cohort study of neonates with NE conducted at Kawempe National Referral Hospital, Kampala, Uganda. Neurological investigations include continuous video electroencephalography (EEG) (days 1-4), serial cranial ultrasound imaging, and neonatal brain Magnetic Resonance Imaging and Spectroscopy (MRI/ MRS) (day 10-14). Neurodevelopmental follow-up will be continued to 18-24 months of age including Prechtl’s Assessment of General Movements, Bayley Scales of Infant Development, and a formal scored neurological examination. The primary outcome will be death and moderate-severe neurodevelopmental impairment at 18-24 months. Findings will be used to inform explorative science and larger trials, aiming to develop urgently needed neuroprotective and neurorestorative interventions for NE applicable for use in diverse settings. DISCUSSION: The primary aims of the study are to assess the feasibility of establishing a facility-based cohort of children with NE in Uganda, to enhance our understanding of NE in a low-resource sub-Saharan African setting and provide infrastructure to conduct high-quality research on neuroprotective/ neurorestorative strategies to reduce death and disability from NE. Specific objectives are to establish a NE cohort, in order to 1) investigate the clinical course, aetiology, nature and timing of perinatal brain injury; 2) describe electrographic activity and quantify seizure burden and the relationship with adverse outcomes, and; 3) develop capacity for neonatal brain MRI/S and examine associations with early neurodevelopmental outcomes