Application of advanced neural networks in hypoglycemia detection system

University of Technology, Sydney. Faculty of Engineering and Information Technology.Hypoglycemia is the medical term for a state produced by lower levels of blood glucose. It represents a significant hazard in patients with Type 1 diabetes mellitus (TlDM) which is a chronic medical condition that occurs when the pancreas produces very little or no insulin. The imperfect insulin replacement places patients with TlDM at increased risk for frequent hypoglycemia. Deficient glucose counter-regulation in TlDM patients may even lead to severe hypoglycaemia even with modest insulin elevations. It is very dangerous and can even lead to neurological damage or death. Thus, continuous monitoring of hypoglycemic episodes is important in order to avoid major health complications. Conventionally, the detection of hypoglycemia is performed by puncturing the fingertip of patients and estimate the blood glucose level (BGL) as well as the stage of hypoglycemia. However, the direct monitoring of BGL by extracting blood sample is inconvenient and uncomfortable, a more appealing preposition for preventing hypoglycemia is to monitor changes in relevant physiological parameters. Findings from numerous studies indicate that sudden nocturnal death in type 1 diabetes is thought to be due to ECG QT prolongation with subsequent ventricular tachyarrhythmia in response to nocturnal hypoglycaemia. Though several parameters can be monitored, the most common physiological parameters to be effected from a hypoglycemic reaction are heart rate (HR) and corrected QT interval (QTc) of the ECG signal. Considering the real-time physiological parameters (HR and QTc) changes during hypoglycemia, a non-invasive monitoring of glycemic level is predicted for the hypoglycemia. The topic of this thesis is covered by novel methodologies for the non-invasive hypoglycemia detection system by analyzing the behavioral changes of physiological parameters such as HR and QTc. These algorithms are comprised of three different classification techniques, i) variable translation wavelet neural network (VTWNN), ii) multiple regression-based combinational neural logic network (MR-NLN) and iii) rough-block-based neural network (R-BBNN). By taking the advantages of these proposed network structures, the performance in terms of sensitivity and specificity of non-invasive hypoglycemia monitoring system is improved. The first proposed algorithm is VTWNN in which the wavelets are used as transfer functions in the hidden layer of the network. The network parameters, such as the translation parameters of the wavelets are variable depending on the network inputs. Due to the variable translation parameters, the proposed VTWKN has the ability to model the inputoutput function with input-dependent network parameters. Effectively, it is an adaptive network capable of handling different input patterns and exhibits a better performance. With the adaptive nature, the network provides a better performance and increases the learning ability. For conventional wavelet neural network, a fixed set of weight is offered after the training process and fail to capture nonstationary nature of ECG signal. To overcome with this problem, VTWNN with multiscale wavelet function is firstly introduced in this thesis. With the variable translation parameter, the proposed VTWNN gives faster learning ability with better generalization. The second algorithm, MR-NLN is systematically designed which is based on the characteristics of application. Its design is based on the binary logic gates (AND, OR and NOT) in which the truth table and K-map are constructed and it depends on the knowledge of application. Because the logic theory are used in the network design, the structure becomes systematic and simpler compared to other conventional neural networks (NNs) and enhance the training performance. Traditionally, the conventional NN s with the same structure are applied to handle different applications. The optimal performance may not always guaranteed due to different characteristics of applications. In real-world applications, the knowledge based-neural network that understands all the characteristics of practical applications are preferred for optimal performance. In conventional NNs, the redundant connections and weights of conventional neural networks make the number of network parameters unnecessarily large and downgrades the training performance. But for neural logic network (NLN), the structure becomes simpler. The third algorithm focuses on the hybridization technology using rough sets concepts and neural computing for decision and classification purposes. Based on the rough set properties, the input signal is partitioned to a predictable (certain) part and random (uncertain) part. In this way, the selected block-based neural network (BBNN) is designed to deal only with the boundary region which mainly consists of a random part of applied input signal and caused inaccurate modeling of data set. Due to the rough set properties and the adaptability of BBNN's flexible structures in dynamic environments, the classification performance is improved. Owing to different characteristics of neural network (NN) applications, a conventional neural network with a common structure may not be able to handle every applications. Based on the knowledge of application, BBNN is selected as a suitable classifier due to its modular characteristics and ability in evolving the size and structure of the network. To obtain the optimal set of proposed network parameters, a global learning optimization algorithm called hybrid particle swarm optimization with wavelet mutation (HPSOWM) is introduced in this thesis. Compared to other stochastic optimization methods, the hybrid HPSO\VM has comparable or even superior search performance for some hard optimization problems with faster and more stable convergence rates. During the training process, a fitness function which is characterized by the proposed network design parameters is optimized by reproducing a better fitness value. The proposed systems is validated using clinical trial conducted at the Princess Margaret Hospital for Children in Perth, Western Australia, Australia. A total of 15 children with 529 data points (ages between 14.6 to 16.6 years) with Type 1 diabetes volunteered for the 10-hour overnight for natural occurrence of nocturnal hypoglycemia. Prior to the application of the algorithms, the correlation between the measured physiological parameters, HR and QTc and the actual BGL for each subject were analyzed. The feature extracted ECG parameters, HR and QTc significantly increased under hypoglycemic conditions (BGL ≤ 3.3mmol/l) according to their respective p values, HR (p < 0.06) and QTc (p < 0.001). The observation on these changes within the physiological parameters have provided the groundwork for model classification algorithms.</p

Block based neural network for hypoglycemia detection

In this paper, evolvable block based neural network (BBNN) is presented for detection of hypoglycemia episodes. The structure of BBNN consists of a two-dimensional (2D) array of fundamental blocks with four variable input-output nodes and weight connections. Depending on the structure settings, each block can have one of four different internal configurations. To provide early detection of hypoglycemia episodes, the physiological parameters such as heart rate (HR) and corrected QT interval (QTc) of electrocardiogram (ECG) signal are used as the inputs of BBNN. The overall structure and weights of BBNN are optimized by an evolutionary algorithm called hybrid particle swarm optimization with wavelet mutation (HPSOWM). The optimized structures and weights of BBNN are capable to compensate large variations of ECG patterns caused by individual and temporal difference since a fixed structure classifiers are easy to fail to trace ECG signals with large variations. The ECG data of 15 patients are organized into a training set, a testing set and a validation set, each of which has randomly selected 5 patients. The simulation results shows that the proposed algorithm, BBNN with HPSOWM can successfully detect the hypoglycemic episodes in T1DM in term of testing sensitivity (76.74%) and test specificity (50.91%). © 2011 IEEE

Deep learning framework for detection of hypoglycemic episodes in children with type 1 diabetes

© 2016 IEEE. Most Type 1 diabetes mellitus (T1DM) patients have hypoglycemia problem. Low blood glucose, also known as hypoglycemia, can be a dangerous and can result in unconsciousness, seizures and even death. In recent studies, heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal are found as the most common physiological parameters to be effected from hypoglycemic reaction. In this paper, a state-of-the-art intelligent technology namely deep belief network (DBN) is developed as an intelligent diagnostics system to recognize the onset of hypoglycemia. The proposed DBN provides a superior classification performance with feature transformation on either processed or un-processed data. To illustrate the effectiveness of the proposed hypoglycemia detection system, 15 children with Type 1 diabetes were volunteered overnight. Comparing with several existing methodologies, the experimental results showed that the proposed DBN outperformed and achieved better classification performance

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

© 2016 ISA Hypoglycemia is a very common in type 1 diabetic persons and can occur at any age. It is always threatening to the well-being of patients with Type 1 diabetes mellitus (T1DM) since 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. In this paper, we proposed a non-invasive hypoglycemia monitoring system using the physiological parameters of electrocardiography (ECG) signal. To enhance the detection accuracy, extreme learning machine (ELM) is developed to recognize the presence of hypoglycemia. A clinical study of 16 children with T1DM is given to illustrate the good performance of ELM

Hybrid PSO-based variable translation wavelet neural network and its application to hypoglycemia detection system

To provide the detection of hypoglycemic episodes in Type 1 diabetes mellitus, hypoglycemia detection system is developed by the use of variable translation wavelet neural network (VTWNN) in this paper. A wavelet neural network with variable translation parameter is selected as a suitable classifier because of its excellent characteristics in capturing nonstationary signal analysis and nonlinear function modeling. Due to the variable translation parameters, the network becomes an adaptive network and provides better classification performance. An improved hybrid particle swarm optimization is used to train the parameters of VTWNN. Using the proposed classifier, a sensitivity of 81.40 % and a specificity of 50.91 % were achieved. The comparison results also show that the proposed detection system performs well in terms of good sensitivity and acceptable specificity. © 2012 Springer-Verlag London Limited

Combinational neural logic system and its industrial application on hypoglycemia monitoring system

In this paper, a combinational neural logic network (NLN) with the neural-Logic-AND, -OR and -NOT gates is applied on the development of non-invasive hypoglycemia monitoring system. It is an alarm system which measured physiological parameters of electrocardiogram (ECG) signal and determine the onset of hypoglycemia by use of proposed NLN. Due to different nature of application, conventional neural networks (NNs) with common structure may not always guarantee the optimal solution. Based on knowledge of application, the proposed NLN is designed systematically in order to incorporate the characteristics of application into the structure of proposed network. The parameter of the proposed NLN will be trained by hybrid particle swarm optimization with wavelet mutation (HPSOWM). The proposed NLN will be practically analyzed using real data sets collected from 15 children (569 data sets) with Type 1 diabetes at the Department of Health, Government of Western Australia. By using the proposed method, the detection performance is enhanced. Compared with other conventional NNs, the proposed NLN gives better performance in terms of sensitivity and specificity. © 2013 IEEE

A novel extreme learning machine for hypoglycemia detection

© 2014 IEEE. Hypoglycemia is a common side-effect of insulin therapy for patients with type 1 diabetes mellitus (T1DM) and 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 T1DM since more severe hypoglycemia leads to seizures or loss of consciousness and the possible development of permanent brain dysfunction under certain circumstances. Thus, an accurate early detection on hypoglycemia is an important research topic. With the use of new emerging technology, an extreme learning machine (ELM) based hypoglycemia detection system is developed to recognize the presence of hypoglycemic episodes. From a clinical study of 16 children with T1DM, natural occurrence of nocturnal hypoglycemic episodes are associated with increased heart rates (p < 0.06) and increased corrected QT intervals (p < 0.001). The overall data were organized into a training set with 8 patients (320 data points) and a testing set with 8 patients (269 data points). By using the ELM trained feed-forward neural network (ELM-FFNN), the testing sensitivity (true positive) and specificity (true negative) for detection of hypoglycemia is 78 and 60% respectability

EEG-based driver fatigue detection using hybrid deep generic model

© 2016 IEEE. Classification of electroencephalography (EEG)-based application is one of the important process for biomedical engineering. Driver fatigue is a major case of traffic accidents worldwide and considered as a significant problem in recent decades. In this paper, a hybrid deep generic model (DGM)-based support vector machine is proposed for accurate detection of driver fatigue. Traditionally, a probabilistic DGM with deep architecture is quite good at learning invariant features, but it is not always optimal for classification due to its trainable parameters are in the middle layer. Alternatively, Support Vector Machine (SVM) itself is unable to learn complicated invariance, but produces good decision surface when applied to well-behaved features. Consolidating unsupervised high-level feature extraction techniques, DGM and SVM classification makes the integrated framework stronger and enhance mutually in feature extraction and classification. The experimental results showed that the proposed DBN-based driver fatigue monitoring system achieves better testing accuracy of 73.29 % with 91.10 % sensitivity and 55.48 % specificity. In short, the proposed hybrid DGM-based SVM is an effective method for the detection of driver fatigue in EEG

Improving EEG-based driver fatigue classification using sparse-deep belief networks

© 2017 Chai, Ling, San, Naik, Nguyen, Tran, Craig and Nguyen. This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively

An affordable, quality-assured community-based system for high-resolution entomological surveillance of vector mosquitoes that reflects human malaria infection risk patterns.

ABSTRACT: BACKGROUND: More sensitive and scalable entomological surveillance tools are required to monitor low levels of transmission that are increasingly common across the tropics, particularly where vector control has been successful. A large-scale larviciding programme in urban Dar es Salaam, Tanzania is supported by a community-based (CB) system for trapping adult mosquito densities to monitor programme performance. Methodology An intensive and extensive CB system for routine, longitudinal, programmatic surveillance of malaria vectors and other mosquitoes using the Ifakara Tent Trap (ITT-C) was developed in Urban Dar es Salaam, Tanzania, and validated by comparison with quality assurance (QA) surveys using either ITT-C or human landing catches (HLC), as well as a cross-sectional survey of malaria parasite prevalence in the same housing compounds. RESULTS: Community-based ITT-C had much lower sensitivity per person-night of sampling than HLC (Relative Rate (RR) [95% Confidence Interval (CI)] = 0.079 [0.051, 0.121], P < 0.001 for Anopheles gambiae s.l. and 0.153 [0.137, 0.171], P < 0.001 for Culicines) but only moderately differed from QA surveys with the same trap (0.536 [0.406,0.617], P = 0.001 and 0.747 [0.677,0.824], P < 0.001, for An. gambiae or Culex respectively). Despite the poor sensitivity of the ITT per night of sampling, when CB-ITT was compared with QA-HLC, it proved at least comparably sensitive in absolute terms (171 versus 169 primary vectors caught) and cost-effective (153US$versus 187US$ per An. gambiae caught) because it allowed more spatially extensive and temporally intensive sampling (4284 versus 335 trap nights distributed over 615 versus 240 locations with a mean number of samples per year of 143 versus 141). Despite the very low vectors densities (Annual estimate of about 170 An gambiae s.l bites per person per year), CB-ITT was the only entomological predictor of parasite infection risk (Odds Ratio [95% CI] = 4.43[3.027,7. 454] per An. gambiae or Anopheles funestus caught per night, P =0.0373). Discussion and conclusion CB trapping approaches could be improved with more sensitive traps, but already offer a practical, safe and affordable system for routine programmatic mosquito surveillance and clusters could be distributed across entire countries by adapting the sample submission and quality assurance procedures accordingly