Non-invasive hypoglycemia monitoring system using extreme learning machine for Type 1 diabetes

Abstract

Hypoglycemia (low blood glucose level) is a medical emergency and is a very common in type 1 diabetic persons and can occur at any age. It is the major limiting factor to maintain tight glycemic control. The deficiency in glucose counter-regulation may even lead to severe hypoglycaemia. It is always threatening to the well-being of patients with Type 1 diabetes mellitus (T1DM) since more severe hypoglycemia leads to seizures or loss of consciousness and the possible development of permanent brain dysfunction under certain circumstances . Because of that, an accurate continuing hypoglycemia monitoring system is a very important medical device for diabetic patients. Traditionally, different electrochemical glucose meters are adopted to measure the blood glucose. However, they are invasive, discontinuing and uncomfortable methods or painful for patients. In this paper, we proposed a noninvasive hypoglycemia monitoring system using the physiological parameters of electrocardiography (ECG) signal such as heart rate and corrected QT intervals. To enhance the detection accuracy of the hypoglycemia, a new emerging technology called extreme learning machine (ELM) is developed to recognize the presence of hypoglycemic episodes. ELM have both universal approximation and classification capabilities and provides efficient unified solutions to generalize feed-forward neural networks. A real clinical study of sixteen children with T1DM are given in this paper to illustrate the performance of ELM and successfully applied to a non-invasive hypoglycemia monitoring system. The natural occurrence of nocturnal hypoglycemic episodes are associated with increased heart rates (p < 0.06) and increased corrected QT intervals (p < 0.001). For comparison purpose, different computational intelligence technologies such as swarm based neural network, multiple regression based-fuzzy inference system, and fuzzy system were applied to this hypoglycemia monitoring system. By using the proposed ELM-based neural network (ELM-NN), the testing sensitivity (true positive) and specificity (true negative) for detection of hypoglycemia is better than the other intelligence methods with faster training time.

Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG

“…Other studies attempted to detect hypoglycemia through non-invasive monitoring using features extracted from the ECG signal. Studies co-authored by Prof. Hung T. Nguyen 41,42,44,53,54,75,76 involved nocturnal hypoglycemia detection in 15 type 1 diabetic children using different machine learning techniques (extreme learning 41 , hybrid swarm optimization 53 , neural networks 54 , genetic algorithms 44 , and a few others), using as inputs different ECG parameters computed from 5 or 10 minutes ECG excerpts, and achieving promissing sensitivity and specificity. For example, the more recent studies, proposed models based on a neural logic approach 76 , obtaining 79.07% sensitivity and 53.64% specificity, deep belief network approach 77 , achieving 80% sensitivity and 50% specificity, models based on extreme learning approach, obtaining 78% sensitivity and 60% specificity.…”

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

“…The applications are summarized in Table 3. The applications on diabetes are type 2 diabetes diagnosis [103][104][105], prediction of fasting plasma glucose status [106], analysis of predictive power of hypertriglyceridemic waist phenotype [107], detection of hypoglycemic episodes in children [108], prediction of protein-protein interaction [109], prediction of vascular occlusion [110], prediction of development of liver cancer for diabetes sufferers [111] and detection of microalbuminuria [112] related to diabetes. ACO = ant colony optimization; AUC = area under the curve; DT = decision tree; FL = fuzzy logic; KNN = k-nearest neighbor; LR = logistic regression; NBC = Naive Bayes classifier; NN = neural network; PSO = particle swarm optimization; RF = random forest; SVM = support vector machine; WPS = wolf pack search.…”

“…Due to the multitude of smart healthcare applications, only four applications in the field of diseases diagnosis, cardiovascular diseases [82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97][98][99], diabetes mellitus [100][101][102][103][104][105][106][107][108][109][110][111][112], Alzheimer's disease and other forms of dementias [113][114][115][116][117][118][119][120][121][122][123][124][125][126], and tuberculosis [127][128][129][130][13...…”

Noninvasive Glucose Measurement Using Machine Learning and Neural Network Methods and Correlation with Heart Rate Variability

“…A noninvasive nocturnal hypoglycemia monitoring system for type 1 diabetes patients is presented by Ling et al [95]. The system uses an extreme learning machine-based neural network model.…”