Assessment of inter-examiner agreement and variability in the manual classification of auditory brainstem response

Abstract Background: The analysis of the Auditory Brainstem Response (ABR) is of fundamental importance to the investigation of the auditory system behaviour, though its interpretation has a subjective nature because of the manual process employed in its study and the clinical experience required for its analysis. When analysing the ABR, clinicians are often interested in the identification of ABR signal components referred to as Jewett waves. In particular, the detection and study of the time when these waves occur (i.e., the wave latency) is a practical tool for the diagnosis of disorders affecting the auditory system. Significant differences in inter-examiner results may lead to completely distinct clinical interpretations of the state of the auditory system. In this context, the aim of this research was to evaluate the inter-examiner agreement and variability in the manual classification of ABR. Methods: A total of 160 ABR data samples were collected, for four different stimulus intensity (80dBHL, 60dBHL, 40dBHL and 20dBHL), from 10 normal-hearing subjects (5 men and 5 women, from 20 to 52 years). Four examiners with expertise in the manual classification of ABR components participated in the study. The Bland-Altman statistical method was employed for the assessment of inter-examiner agreement and variability. The mean, standard deviation and error for the bias, which is the difference between examiners’ annotations, were estimated for each pair of examiners. Scatter plots and histograms were employed for data visualization and analysis. Results: In most comparisons the differences between examiner’s annotations were below 0.1 ms, which is clinically acceptable. In four cases, it was found a large error and standard deviation (>0.1 ms) that indicate the presence of outliers and thus, discrepancies between examiners. Conclusions: Our results quantify the inter-examiner agreement and variability of the manual analysis of ABR data, and they also allows for the determination of different patterns of manual ABR analysis.

A unified neurofuzzy model for classification

This work proposes a unified neurofuzzy modelling scheme. To begin with, the initial fuzzy base construction method is based on fuzzy clustering utilising a Gaussian mixture model (GMM) combined with the analysis of covariance (ANOVA) decomposition in order to obtain more compact univariate and bivariate membership functions over the subspaces of the input features. The mean and covariance of the Gaussian membership functions are found by the expectation maximisation (EM) algorithm with the merit of revealing the underlying density distribution of system inputs. The resultant set of membership functions forms the basis of the generalised fuzzy model (GFM) inference engine. The model structure and parameters of this neurofuzzy model are identified via the supervised subspace orthogonal least square (OLS) learning. Finally, instead of providing deterministic class label as model output by convention, a logistic regression model is applied to present the classifier’s output, in which the sigmoid type of logistic transfer function scales the outputs of the neurofuzzy model to the class probability. Experimental validation results are presented to demonstrate the effectiveness of the proposed neurofuzzy modelling scheme.

The classification and application of toxic Chinese Materia Medica

Many important drugs in the Chinese materia medica (CMM) are known to be toxic, and it has long been recognized in classical Chinese medical theory that toxicity can arise directly from the components of a single CMM or may be induced by an interaction between combined CMM. Traditional Chinese Medicine presents a unique set of pharmaceutical theories that include particular methods for processing, combining and decocting, and these techniques contribute to reducing toxicity as well as enhancing efficacy. The current classification of toxic CMM drugs, traditional methods for processing toxic CMM and the prohibited use of certain combinations, is based on traditional experience and ancient texts and monographs, but accumulating evidence increasingly supports their use to eliminate or reduce toxicity. Modern methods are now being used to evaluate the safety of CMM; however, a new system for describing the toxicity of Chinese herbal medicines may need to be established to take into account those herbs whose toxicity is delayed or otherwise hidden, and which have not been incorporated into the traditional classification. This review explains the existing classification and justifies it where appropriate, using experimental results often originally published in Chinese and previously not available outside China.

Fractional order and non-linear system identification algorithms for biomedical applications

We discuss the modelling of dielectric responses of amorphous biological samples. Such samples are commonly encountered in impedance spectroscopy studies as well as in UV, IR, optical and THz transient spectroscopy experiments and in pump-probe studies. In many occasions, the samples may display quenched absorption bands. A systems identification framework may be developed to provide parsimonious representations of such responses. To achieve this, it is appropriate to augment the standard models found in the identification literature to incorporate fractional order dynamics. Extensions of models using the forward shift operator, state space models as well as their non-linear Hammerstein-Wiener counterpart models are highlighted. We also discuss the need to extend the theory of electromagnetically excited networks which can account for fractional order behaviour in the non-linear regime by incorporating nonlinear elements to account for the observed non-linearities. The proposed approach leads to the development of a range of new chemometrics tools for biomedical data analysis and classification.

Classification in e-procurement

Three coupled knowledge transfer partnerships used pattern recognition techniques to produce an e-procurement system which, the National Audit Office reports, could save the National Health Service £500 m per annum. An extension to the system, GreenInsight, allows the environmental impact of procurements to be assessed and savings made. Both systems require suitable products to be discovered and equivalent products recognised, for which classification is a key component. This paper describes the innovative work done for product classification, feature selection and reducing the impact of mislabelled data.

Modelling personal thermal sensations using C-Support Vector Classification (C-SVC) algorithm

The personalised conditioning system (PCS) is widely studied. Potentially, it is able to reduce energy consumption while securing occupants’ thermal comfort requirements. It has been suggested that automatic optimised operation schemes for PCS should be introduced to avoid energy wastage and discomfort caused by inappropriate operation. In certain automatic operation schemes, personalised thermal sensation models are applied as key components to help in setting targets for PCS operation. In this research, a novel personal thermal sensation modelling method based on the C-Support Vector Classification (C-SVC) algorithm has been developed for PCS control. The personal thermal sensation modelling has been regarded as a classification problem. During the modelling process, the method ‘learns’ an occupant’s thermal preferences from his/her feedback, environmental parameters and personal physiological and behavioural factors. The modelling method has been verified by comparing the actual thermal sensation vote (TSV) with the modelled one based on 20 individual cases. Furthermore, the accuracy of each individual thermal sensation model has been compared with the outcomes of the PMV model. The results indicate that the modelling method presented in this paper is an effective tool to model personal thermal sensations and could be integrated within the PCS for optimised system operation and control.

Evaluation of simulated sea-ice concentrations from sea-ice/ocean models using satellite data and polynya classification methods

Sea-ice concentrations in the Laptev Sea simulated by the coupled North Atlantic-Arctic Ocean-Sea-Ice Model and Finite Element Sea-Ice Ocean Model are evaluated using sea-ice concentrations from Advanced Microwave Scanning Radiometer-Earth Observing System satellite data and a polynya classification method for winter 2007/08. While developed to simulate largescale sea-ice conditions, both models are analysed here in terms of polynya simulation. The main modification of both models in this study is the implementation of a landfast-ice mask. Simulated sea-ice fields from different model runs are compared with emphasis placed on the impact of this prescribed landfast-ice mask. We demonstrate that sea-ice models are not able to simulate flaw polynyas realistically when used without fast-ice description. Our investigations indicate that without landfast ice and with coarse horizontal resolution the models overestimate the fraction of open water in the polynya. This is not because a realistic polynya appears but due to a larger-scale reduction of ice concentrations and smoothed ice-concentration fields. After implementation of a landfast-ice mask, the polynya location is realistically simulated but the total open-water area is still overestimated in most cases. The study shows that the fast-ice parameterization is essential for model improvements. However, further improvements are necessary in order to progress from the simulation of large-scale features in the Arctic towards a more detailed simulation of smaller-scaled features (here polynyas) in an Arctic shelf sea.

Insights into the Specificity of Thioredoxin Reductase-Thioredoxin Interactions. A Structural and Functional Investigation of the Yeast Thioredoxin System

The enzymatic activity of thioredoxin reductase enzymes is endowed by at least two redox centers: a flavin and a dithiol/disulfide CXXC motif. The interaction between thioredoxin reductase and thioredoxin is generally species-specific, but the molecular aspects related to this phenomenon remain elusive. Here, we investigated the yeast cytosolic thioredoxin system, which is composed of NADPH, thioredoxin reductase (ScTrxR1), and thioredoxin 1 (ScTrx1) or thioredoxin 2 (ScTrx2). We showed that ScTrxR1 was able to efficiently reduce yeast thioredoxins (mitochondrial and cytosolic) but failed to reduce the human and Escherichia coli thioredoxin counterparts. To gain insights into this specificity, the crystallographic structure of oxidized ScTrxR1 was solved at 2.4 angstrom resolution. The protein topology of the redox centers indicated the necessity of a large structural rearrangement for FAD and thioredoxin reduction using NADPH. Therefore, we modeled a large structural rotation between the two ScTrxR1 domains (based on the previously described crystal structure, PDB code 1F6M). Employing diverse approaches including enzymatic assays, site-directed mutagenesis, amino acid sequence alignment, and structure comparisons, insights were obtained about the features involved in the species-specificity phenomenon, such as complementary electronic parameters between the surfaces of ScTrxR1 and yeast thioredoxin enzymes and loops and residues (such as Ser(72) in ScTrx2). Finally, structural comparisons and amino acid alignments led us to propose a new classification that includes a larger number of enzymes with thioredoxin reductase activity, neglected in the low/high molecular weight classification.

Phase behavior of the orange essential oil/sodium bis(2-ethylhexyl)sulfosuccinate/water system

The ternary phase diagram for the orange essential oil (OEO)/sodium bis(2-ethylhexyl)sulfosuccinate (AOT)/water system was constructed at 25 degrees C. It indicates a large single phase region, comprising an isotropic water-in-oil (W/O) microemulsion (ME) phase (L(2)), a liquid crystal (LC) (lamellar or hexagonal) and a large unstable emulsion phase that separates in two phases of normal and reverse micelles (L(1) and L(2)). In this communication the properties of the ME are investigated by viscosity, electric conductivity and small angle X-ray scattering (SAXS) indicating that the isotropic ME phase exhibits different behaviors depending on composition. At low water content low viscous ""dry"" surfactant structures are formed, whereas at higher water content higher viscous water droplets are formed. The experimental data allow the determination of the transition from ""dry"" to the water droplet structures within the L(2) phase. SAXS analyses have also been performed for selected LC samples. (C) 2009 Elsevier B.V. All rights reserved.

Fluoxetine and norfluoxetine analysis by direct injection of human plasma in a column switching liquid chromatographic system

A column switching LC method is presented for the analysis of fluoxetine (FLU) and norfluoxetine (NFLU) by direct injection of human plasma using a lab-made restricted access media (RAM) column. A RAM-BSA-octadecyl silica (C-18) column (40 min x 4.6 mm, 10 mu m) is evaluated in both backflush and foreflush elution modes and coupled with a C-18 lab-made (50 mm x 4.6 mm, 3 pm) analytical column in order to perform online sample preparation. Direct injection of 100 mu L, of plasma samples is possible with the developed approach. In addition, reduction of sample handling is obtained when compared with traditional liquid-liquid extraction (LLE) and SPE. The total analysis time is around 20 min. A LOQ of 15 ng/mL is achieved in a concentration range of 15-500 ng/mL, allowing the therapeutic drug monitoring of clinical samples. The precision values achieved are lower than 15% for all the evaluated points with adequate recovery and accuracy. Furthermore, no matrix interferences are found in the analysis and the proposed method shows to be an adequate alternative for analysis of FLU in plasma.

Speech Assessment for the Classification of Hypokinetic Dysthria in Parkinson Disease

The aim of this thesis is to investigate computerized voice assessment methods to classify between the normal and Dysarthric speech signals. In this proposed system, computerized assessment methods equipped with signal processing and artificial intelligence techniques have been introduced. The sentences used for the measurement of inter-stress intervals (ISI) were read by each subject. These sentences were computed for comparisons between normal and impaired voice. Band pass filter has been used for the preprocessing of speech samples. Speech segmentation is performed using signal energy and spectral centroid to separate voiced and unvoiced areas in speech signal. Acoustic features are extracted from the LPC model and speech segments from each audio signal to find the anomalies. The speech features which have been assessed for classification are Energy Entropy, Zero crossing rate (ZCR), Spectral-Centroid, Mean Fundamental-Frequency (Meanf0), Jitter (RAP), Jitter (PPQ), and Shimmer (APQ). Naïve Bayes (NB) has been used for speech classification. For speech test-1 and test-2, 72% and 80% accuracies of classification between healthy and impaired speech samples have been achieved respectively using the NB. For speech test-3, 64% correct classification is achieved using the NB. The results direct the possibility of speech impairment classification in PD patients based on the clinical rating scale.

Insights into the Specificity of Thioredoxin Reductase-Thioredoxin Interactions. A Structural and Functional Investigation of the Yeast Thioredoxin System

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Aquatic weed automatic classification using machine learning techniques

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

LC Evaluation of In Vitro Release of AZT from Microemulsions

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Wear Particle Classifier System Based on an Artificial Neural Network

This paper describes a method of identifying morphological attributes that classify wear particles in relation to the wear process from which they originate and permit the automatic identification without human expertise. The method is based on the use of Multi Layer Perceptron (MLP) for analysis of specific types of microscopic wear particles. The classification of the wear particles was performed according to their morphological attributes of size and aspect ratio, among others. (C) 2010 Journal of Mechanical Engineering. All rights reserved.