• Bayesian assessment of newborn brain maturity from two-channel sleep electroencephalograms

      Jakaite, Livija; Schetinin, Vitaly; Maple, Carsten; University of Bedfordshire (2012)
      Newborn brain maturity can be assessed by expert analysis of maturity-related patterns recognizable in polysomnograms. Since 36 weeks most of these patterns become recognizable in EEG exclusively, particularly, in EEG recorded via the two central-temporal channels. The use of such EEG recordings enables experts to minimize the disturbance of sleep, preparation time as well as the movement artifacts. We assume that the brain maturity of newborns aged 36 weeks and older can be automatically assessed from the 2-channel sleep EEG as accurately as by expert analysis of the full polysomnographic information. We use Bayesian inference to test this assumption and assist experts to obtain the full probabilistic information on the EEG assessments. The Bayesian methodology is feasibly implemented with Monte Carlo integration over areas of high posterior probability density, however the existing techniques tend to provide biased assessments in the absence of prior information required to explore a model space in detail within a reasonable time. In this paper we aim to use the posterior information about EEG features to reduce possible bias in the assessments. The performance of the proposed method is tested on a set of EEG recordings.
    • The Bayesian decision tree technique using an adaptive sampling scheme

      Schetinin, Vitaly; Krzanowski, Wojtek; Maple, Carsten (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2007)
      Decision trees (DTs) provide an attractive classification scheme because clinicians responsible for making reliable decisions can easily interpret them. Bayesian averaging over DTs allows clinicians to evaluate the class posterior distribution and therefore to estimate the risk of making misleading decisions. The use of Markov chain Monte Carlo (MCMC) methodology of stochastic sampling makes the Bayesian DT technique feasible to perform. The Reversible Jump (RJ) extension of MCMC allows sampling from DTs of different sizes. However, the RJ MCMC process may become stuck in a particular DT far away from the region with maximal posterior. This negative effect can be mitigated by averaging the DTs obtained in different starts. In this paper we describe a new approach based on an adaptive sampling scheme. The performances of Bayesian DT techniques with the restarting and adaptive strategies are compared on a synthetic dataset as well as on some medical datasets. By quantitatively evaluating the classification uncertainty, we found that the adaptive strategy is superior to the restarting strategy.
    • Bayesian decision trees for predicting survival of patients: a study on the US National Trauma Data Bank

      Schetinin, Vitaly; Jakaite, Livija; Jakaitis, Janis; Krzanowski, Wojtek; University of Bedfordshire; University of Exeter (2013)
      Trauma and Injury Severity Score (TRISS) models have been developed for predicting the survival probability of injured patients the majority of which obtain up to three injuries in six body regions. Practitioners have noted that the accuracy of TRISS predictions is unacceptable for patients with a larger number of injuries. Moreover, the TRISS method is incapable of providing accurate estimates of predictive density of survival, that are required for calculating confidence intervals. In this paper we propose Bayesian in ference for estimating the desired predictive density. The inference is based on decision tree models which split data along explanatory variables, that makes these models interpretable. The proposed method has outperformed the TRISS method in terms of accuracy of prediction on the cases recorded in the US National Trauma Data Bank. The developed method has been made available for evaluation purposes as a stand-alone application.
    • A Bayesian model averaging methodology for detecting EEG artifacts

      Schetinin, Vitaly; Maple, Carsten (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2007)
      In this paper we describe a Bayesian Model Averaging (BMA) methodology developed for detecting artifacts in electroencephalograms (EEGs). The EEGs can be heavily corrupted by cardiac, eye movement, muscle and noise artifacts, so that EEG experts need to automatically detect them with a given level of confidence. In theory, the BMA methodology allows experts to evaluate the confidence in decision making most accurately. However, the non- stationary nature of EEGs makes the use of this methodology difficult. In our experiments with the sleep EEGs, the proposed BMA technique is shown to provide a better performance in terms of predictive accuracy.
    • Classification of newborn EEG maturity with Bayesian averaging over decision trees

      Schetinin, Vitaly; Jakaite, Livija (Elsevier, 2012-08)
      EEG experts can assess a newborn’s brain maturity by visual analysis of age-related patterns in sleep EEG. It is highly desirable to make the results of assessment most accurate and reliable. However, the expert analysis is limited in capability to provide the estimate of uncertainty in assessments. Bayesian inference has been shown providing the most accurate estimates of uncertainty by using Markov Chain Monte Carlo (MCMC) integration over the posterior distribution. The use of MCMC enables to approximate the desired distribution by sampling the areas of interests in which the density of distribution is high. In practice, the posterior distribution can be multimodal, and so that the existing MCMC techniques cannot provide the proportional sampling from the areas of interest. The lack of prior information makes MCMC integration more difficult when a model parameter space is large and cannot be explored in detail within a reasonable time. In particular, the lack of information about EEG feature importance can affect the results of Bayesian assessment of EEG maturity. In this paper we explore how the posterior information about EEG feature importance can be used to reduce a negative influence of disproportional sampling on the results of Bayesian assessment. We found that the MCMC integration tends to oversample the areas in which a model parameter space includes one or more features, the importance of which counted in terms of their posterior use is low. Using this finding, we proposed to cure the results of MCMC integration and then described the results of testing the proposed method on a set of sleep EEG recordings.
    • Comparing robustness of pairwise and multiclass neural-network systems for face recognition

      Uglov, J.; Jakaite, Livija; Schetinin, Vitaly; Maple, Carsten (2008)
      Noise, corruptions, and variations in face images can seriously hurt the performance of face-recognition systems. To make these systems robust to noise and corruptions in image data, multiclass neural networks capable of learning from noisy data have been suggested. However on large face datasets such systems cannot provide the robustness at a high level. In this paper, we explore a pairwise neural-network system as an alternative approach to improve the robustness of face recognition. In our experiments, the pairwise recognition system is shown to outperform the multiclass-recognition system in terms of the predictive accuracy on the test face images.
    • Computer-aided segmentation and estimation of indices in brain CT scans

      Schetinin, Vitaly; Qureshi, Adnan Nabeel Abid; University of Bedfordshire (City University, London, 2014)
      The importance of neuro-imaging as one of the biomarkers for diagnosis and prognosis of pathologies and traumatic cases is well established. Doctors routinely perform linear measurements on neuro-images to ascertain severity and extent of the pathology or trauma from significant anatomical changes. However, it is a tedious and time consuming process and manually assessing and reporting on large volume of data is fraught with errors and variation. In this paper we present a novel technique for segmentation of significant anatomical landmarks using artificial neural networks and estimation of various ratios and indices performed on brain CT scans. The proposed method is efficient and robust in detecting and measuring sizes of anatomical structures on non-contrast CT scans and has been evaluated on images from subjects with ages between 5 to 85 years. Results show that our method has average ICC of ≥0.97 and, hence, can be used in processing data for further use in research and clinical environment.
    • An evolutionary-based approach to learning multiple decision models from underrepresented data

      Schetinin, Vitaly; Li, Dayou; Maple, Carsten (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2008)
      The use of multiple Decision Models (DMs) enables to enhance the accuracy in decisions and at the same time allows users to evaluate the confidence in decision making. In this paper we explore the ability of multiple DMs to learn from a small amount of verified data. This becomes important when data samples are difficult to collect and verify. We propose an evolutionary-based approach to solving this problem. The proposed technique is examined on a few clinical problems presented by a small amount of data.
    • Feature extraction from electroencephalograms for Bayesian assessment of newborn brain maturity

      Jakaite, Livija; Schetinin, Vitaly; Schult, Joachim (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2011)
      We explored the feature extraction techniques for Bayesian assessment of EEG maturity of newborns in the context that the continuity of EEG is the most important feature for assessment of the brain development. The continuity is associated with EEG “stationarity” which we propose to evaluate with adaptive segmentation of EEG into pseudo-stationary intervals. The histograms of these intervals are then used as new features for the assessment of EEG maturity. In our experiments, we used Bayesian model averaging over decision trees to differentiate two age groups, each included 110 EEG recordings. The use of the proposed EEG features has shown, on average, a 6% increase in the accuracy of age differentiation.
    • Informativeness of sleep cycle features in Bayesian assessment of newborn electroencephalographic maturation

      Schetinin, Vitaly; Jakaite, Livija; Schult, Joachim (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2011)
      Clinical experts assess the newborn brain development by analyzing and interpreting maturity-related features in sleep EEGs. Typically, these features widely vary during the sleep hours, and their informativeness can be different in different sleep stages. Normally, the level of muscle and electrode artifacts during the active sleep stage is higher than that during the quiet sleep that could reduce the informative-ness of features extracted from the active stage. In this paper, we use the methodology of Bayesian averaging over Decision Trees (DTs) to assess the newborn brain maturity and explore the informativeness of EEG features extracted from different sleep stages. This methodology has been shown providing the most accurate inference and estimates of uncertainty, while the use of DT models enables to find the EEG features most important for the brain maturity assessment.
    • Prediction of survival probabilities with Bayesian Decision Trees

      Schetinin, Vitaly; Jakaite, Livija; Krzanowski, Wojtek; University of Bedfordshire; University of Exeter (Elsevier, 2013)
      Practitioners use Trauma and Injury Severity Score (TRISS) models for predicting the survival probability of an injured patient. The accuracy of TRISS predictions is acceptable for patients with up to three typical injuries, but unacceptable for patients with a larger number of injuries or with atypical injuries. Based on a regression model, the TRISS methodology does not provide the predictive density required for accurate assessment of risk. Moreover, the regression model is difficult to interpret. We therefore consider Bayesian inference for estimating the predictive distribution of survival. The inference is based on decision tree models which recursively split data along explanatory variables, and so practitioners can understand these models. We propose the Bayesian method for estimating the predictive density and show that it outperforms the TRISS method in terms of both goodness-of-fit and classification accuracy. The developed method has been made available for evaluation purposes as a stand-alone application.
    • Using a Bayesian averaging model for estimating the reliability of decisions in multimodal biometrics

      Maple, Carsten; Schetinin, Vitaly (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2006)
      The issue of reliable authentication is of increasing importance in modern society. Corporations, businesses and individuals often wish to restrict access to logical or physical resources to those with relevant privileges. A popular method for authentication is the use of biometric data, but the uncertainty that arises due to the lack of uniqueness in biometrics has lead there to be a great deal of effort invested into multimodal biometrics. These multimodal biometric systems can give rise to large, distributed data sets that are used to decide the authenticity of a user. Bayesian model averaging (BMA) methodology has been used to allow experts to evaluate the reliability of decisions made in data mining applications. The use of decision tree (DT) models within the BMA methodology gives experts additional information on how decisions are made. In this paper we discuss how DT models within the BMA methodology can be used for authentication in multimodal biometric systems.