Improving time–frequency domain sleep EEG classification via singular spectrum analysis

Background: Manual sleep scoring is deemed to be tedious and time consuming. Even among automatic methods such as Time-Frequency (T-F) representations, there is still room for more improvement. New method: To optimise the efficiency of T-F domain analysis of sleep electroencephalography (EEG) a novel approach for automatically identifying the brain waves, sleep spindles, and K-complexes from the sleep EEG signals is proposed. The proposed method is based on singular spectrum analysis (SSA). The single-channel EEG signal (C3-A2) is initially decomposed and then the desired components are automatically separated. In addition, the noise is removed to enhance the discrimination ability of features. The obtained T-F features after preprocessing stage are classified using a multi-class support vector machines (SVM) and used for the identification of four sleep stages over three sleep types. Furthermore, to emphasize on the usefulness of the proposed method the automatically-determined spindles are parameterised to discriminate three sleep types. Result: The four sleep stages are classified through SVM twice: with and without preprocessing stage. The mean accuracy, sensitivity, and specificity for before the preprocessing stage are: 71.5 ± 0.11%, 56.1 ± 0.09% and 86.8 ± 0.04% respectively. However, these values increase significantly to 83.6 ± 0.07%, 70.6 ± 0.14% and 90.8 ± 0.03% after applying SSA. Comparison with existing method: The new T-F representation has been compared with the existing benchmarks. Our results prove that, the proposed method well outperforms the previous methods in terms of identification and representation of sleep stages. Conclusion: Experimental results confirm the performance improvement in terms of classification rate and also representative T-F domain

An adaptive filtering approach using supervised SSA for identification of sleep stages from EEG

Purpose: Sleep is a complex physiological state and an indicator of the changes in the brain function similar to those occurring in many psychiatric and neurological conditions. Since visual sleep scoring consuming process, automatic sleep staging methods, also called scoring, hold promise in diagnosing alterations in the sleep process and the sleep EEG more effectively. Method: In this paper, a supervised approach for sleep scoring from single channel EEG signals is proposed. First, a supervised singular spectrum analysis (SSA) which is a subspace based method is used to extract the desired signal for each stage of sleep. Then, two recursive least squares (RLS) adaptive filters are trained and used to identify first and deep sleep stages. Result: The proposed system which can be considered as a filter bank for separating multiple signal subbands is tested using real EEG where the results verify the accuracy of the proposed method. Conclusion: The overall result show the effectiveness of algorithm for detection of sleep stages from EEG signals often characterised by a sharp increase in delta and a rapid decrease in alpha as sleep deepens

Quaternion singular spectrum analysis of electroencephalogram With application in sleep analysis

A novel quaternion-valued singular spectrum analysis (SSA) is introduced for multichannel analysis of electroencephalogram (EEG). The analysis of EEG typically requires the decomposition of data channels into meaningful components despite the notoriously noisy nature of EEG - which is the aim of SSA. However, the singular value decomposition involved in SSA implies the strict orthogonality of the decomposed components, which may not reflect accurately the sources which exhibit similar neural activities. To allow for the modelling of such co-channel coupling, the quaternion domain is considered for the first time to formulate the SSA using the augmented statistics. As an application, we demonstrate how the augmented quaternion-valued SSA (AQSSA) can be used to extract the sources, even at a signal-to-noise ratio as low as -10 dB. To illustrate the usefulness of our quaternion-valued SSA in a rehabilitation setting, we employ the proposed SSA for sleep analysis to extract statistical descriptors for five-stage classification (Awake, N1, N2, N3 and REM). The level of agreement using these descriptors was 74% as quantified by the Cohen's kappa

Tensor based singular spectrum analysis for automatic scoring of sleep EEG

A new supervised approach for decomposition of single channel signal mixtures is introduced in this paper. The performance of the traditional singular spectrum analysis (SSA) algorithm is significantly improved by applying tensor decomposition instead of traditional singular value decomposition (SVD). As another contribution to this subspace analysis method, the inherent frequency diversity of the data has been effectively exploited to highlight the subspace of interest. As an important application, sleep EEG has been analysed and the stages of sleep for the subjects in normal condition, with sleep restriction, and with sleep extension have been accurately estimated and compared with the results of sleep scoring by clinical experts

Bridging the gap: Attending to discontinuity in identification of multiword expressions

We introduce a new method to tag Multiword Expressions (MWEs) using a linguistically interpretable language-independent deep learning architecture. We specifically target discontinuity, an under-explored aspect that poses a significant challenge to computational treatment of MWEs. Two neural architectures are explored: Graph Convolutional Network (GCN) and multi-head self-attention. GCN leverages dependency parse information, and self-attention attends to long-range relations. We finally propose a combined model that integrates complementary information from both through a gating mechanism. The experiments on a standard multilingual dataset for verbal MWEs show that our model outperforms the baselines not only in the case of discontinuous MWEs but also in overall F-score

The ethical identity of law students

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordThis paper uses measures of values, moral outlook and professional identity to explore the ethical and professional identity of law students. We do so in two jurisdictions, surveying 441 students studying in England and Wales and 569 students studying in the US. The survey covers the first and final years of an undergraduate law degree and the postgraduate vocational stage in England and Wales, as well as students in all years of the JD programme in the US. We explore whether law students towards the end of their legal education have ethical identities predictive of less ethical conduct than those at the beginning of their legal education; whether law students intending careers in business law have values and profiles consistent with less ethical conduct than those intending to work for government or individuals; and what factors might explain these differences in ethical outlook. Our findings suggest that ethical identity is strongly associated with gender and career intentions. They also suggest weaker moral identities for students intending to practise business law. Ultimately, our findings support a conclusion that is more nuanced than the predominant theses about the impact of legal education on student ethicality which tend to suggest legal education diminishes ethicality

The Effectiveness of Doctoral Program in Nursing in Iran Based on the Patrick Model

Background: Doctoral program in nursing aims to train nursing professionals and managers to improve the quality of care and ultimately to promote public health. Some critics believe that in Iran this program mostly focuses on training instructors and researchers and neither improves the function and position of nursing discipline nor meets the needs of the community. Objectives: The present study aimed to determine the effectiveness of nursing doctoral program based on the Patrick model from the perspective of nursing doctoral students. Materials and Methods: This cross-sectional study was conducted on 90 nursing students who were conveniently selected from seven nursing schools. A questionnaire designed based on the Patrick model was used. Descriptive statistics, simple and multiple regression analysis were used to analyze the data. Percentage of the effectiveness scores was reported. Results: The mean score of effectiveness of the nursing doctoral program was 84.76 ± 2.73, which assumed a good level. Multiple regression analysis showed that job status and being native in the field of education explains 11% of the variance in the effectiveness score. Conclusions: Although the efficacy of nursing doctoral program is good, however, it needs revision to enhance the outcomes of the program in order to meet public needs and to increase learners’ satisfaction

One dimensional local binary patterns of electroencephalogram signals for detecting Alzheimer's disease

Alzheimer’s disease (AD) is neurodegenerative, caused by the progressive death of brain cells over time. One non-invasive approach to investigate AD is to use electroencephalogram (EEG) signals. The data are usually non-stationary with a strong background activity and noise which makes the analysis difficult leading to low performance in many real world applications including the detection of AD. In this study, we present a method based on local texture changes of EEG signals to differentiate AD patients from the healthy ones, using one-dimensional local binary patterns (1D-LBPs) coupled with support vector machines (SVM). Our proposed method maps the EEG data into a less detailed representation which is less sensitive to noise. A 10 fold cross validation performed at both the epoch and subject level show the discriminancy power of 1D-LBP feature vectors with application to AD data

A Multi Expert Decision Support Tool for the Evaluation of Advanced Wastewater Treatment Trains: A Novel Approach to Improve Urban Sustainability

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Wastewater Treatment (WWT) for water reuse applications has been accepted as a strategic solution in improving water supplies across the globe; however, there are still various challenges that should be overcome. Selection of practical solutions is then required whilst considering technical, environmental, socio-cultural, and financial factors. In this study, a multi expert decision support tool that considers a variety of evaluation criteria is proposed to provide a ranking system for competing advanced WWT technologies in terms of their performance. Two scenarios of water reuse in the contexts of Brazil and Greece are defined, and evaluation is undertaken based on opinions of water reuse experts. The results prove that the tool would successfully facilitate rigorous and methodical analysis in evaluation of WWT technologies for water reuse applications with potential for use under various sets of evaluation criteria, WWT technologies and contexts

The investigation of relevancy between PIAS1 and PIAS2 gene expression and disease severity of multiple sclerosis

Introduction: PIAS1 and PIAS2 (protein inhibitor of activated STAT 1,2) play key roles in the pathogenesis of autoimmune and inflammatory diseases. This study aims to evaluate the gene expression of these factors in multiple sclerosis (MS) patients compared to healthy individuals and correlate them with the severity of MS. Materials and methods: Sixty participants, including 30 patients with MS and 30 healthy controls were studied. The expression of PIAS1 and PIAS2 genes in peripheral blood samples of all participants was measured by real-time PCR. The severity of MS was evaluated using the Expanded Disability Status Scale (EDSS). Finally, we evaluated the correlation between the expression of PIAS1 and PIAS2 genes with disease severity. Results: The expression of PIAS1 gene was increased in patients with MS compared to healthy subjects (P value<.001). Also, there was a significant correlation between the expression of PIAS1 and PIAS2 genes with disease severity according to EDSS. Conclusion: Our study suggests the expression of PIAS1 and PIAS2 genes as a prognostic and diagnostic marker in MS disease. © 2019, © 2019 Taylor & Francis