Deviant disabilities: the exclusion of drug and alcohol addiction from the Equality Act 2010

The Equality Act 2010, in keeping with the Disability Discrimination Act 1995, excludes those identified as drug and alcohol ‘addicted’ from the scope of provisions prohibiting discrimination against disabled people. This article addresses the significance of, and justification for, this exclusion. It begins with a legislative background to the relevant limitation and subsequently examines its rationale according to prevailing legal, medical and sociological discourses. The article then considers the relevance of the discussion for disability rights. Although ‘addiction’, or the preferred term, ‘substance dependence’, is classified as a disability for international systems of disease classification, the relevance of substance dependence for discussion on disability rights, and of disability for discussion on substance dependence, has largely escaped critical comment.

Parkinson’s Disease tremor classification – a comparison between Support Vector Machines and neural networks

Deep Brain Stimulation has been used in the study of and for treating Parkinson’s Disease (PD) tremor symptoms since the 1980s. In the research reported here we have carried out a comparative analysis to classify tremor onset based on intraoperative microelectrode recordings of a PD patient’s brain Local Field Potential (LFP) signals. In particular, we compared the performance of a Support Vector Machine (SVM) with two well known artificial neural network classifiers, namely a Multiple Layer Perceptron (MLP) and a Radial Basis Function Network (RBN). The results show that in this study, using specifically PD data, the SVM provided an overall better classification rate achieving an accuracy of 81% recognition.

Resting tremor classification and detection in Parkinson's disease patients

Parkinson is a neurodegenerative disease, in which tremor is the main symptom. This paper investigates the use of different classification methods to identify tremors experienced by Parkinsonian patients.Some previous research has focussed tremor analysis on external body signals (e.g., electromyography, accelerometer signals, etc.). Our advantage is that we have access to sub-cortical data, which facilitates the applicability of the obtained results into real medical devices since we are dealing with brain signals directly. Local field potentials (LFP) were recorded in the subthalamic nucleus of 7 Parkinsonian patients through the implanted electrodes of a deep brain stimulation (DBS) device prior to its internalization. Measured LFP signals were preprocessed by means of splinting, down sampling, filtering, normalization and rec-tification. Then, feature extraction was conducted through a multi-level decomposition via a wavelettrans form. Finally, artificial intelligence techniques were applied to feature selection, clustering of tremor types, and tremor detection.The key contribution of this paper is to present initial results which indicate, to a high degree of certainty, that there appear to be two distinct subgroups of patients within the group-1 of patients according to the Consensus Statement of the Movement Disorder Society on Tremor. Such results may well lead to different resultant treatments for the patients involved, depending on how their tremor has been classified. Moreover, we propose a new approach for demand driven stimulation, in which tremor detection is also based on the subtype of tremor the patient has. Applying this knowledge to the tremor detection problem, it can be concluded that the results improve when patient clustering is applied prior to detection.

A Genetic Algorithm for the selection of structural MRI features for classification of Mild Cognitive Impairment and Alzheimer's Disease

This work investigates the problem of feature selection in neuroimaging features from structural MRI brain images for the classification of subjects as healthy controls, suffering from Mild Cognitive Impairment or Alzheimer’s Disease. A Genetic Algorithm wrapper method for feature selection is adopted in conjunction with a Support Vector Machine classifier. In very large feature sets, feature selection is found to be redundant as the accuracy is often worsened when compared to an Support Vector Machine with no feature selection. However, when just the hippocampal subfields are used, feature selection shows a significant improvement of the classification accuracy. Three-class Support Vector Machines and two-class Support Vector Machines combined with weighted voting are also compared with the former and found more useful. The highest accuracy achieved at classifying the test data was 65.5% using a genetic algorithm for feature selection with a three-class Support Vector Machine classifier.

Features for detection of Parkinson's disease tremor from local field potentials of the subthalamic nucleus

Deep Brain Stimulation (DBS) is a treatment routinely used to alleviate the symptoms of Parkinson's disease (PD). In this type of treatment, electrical pulses are applied through electrodes implanted into the basal ganglia of the patient. As the symptoms are not permanent in most patients, it is desirable to develop an on-demand stimulator, applying pulses only when onset of the symptoms is detected. This study evaluates a feature set created for the detection of tremor - a cardinal symptom of PD. The designed feature set was based on standard signal features and researched properties of the electrical signals recorded from subthalamic nucleus (STN) within the basal ganglia, which together included temporal, spectral, statistical, autocorrelation and fractal properties. The most characterized tremor related features were selected using statistical testing and backward algorithms then used for classification on unseen patient signals. The spectral features were among the most efficient at detecting tremor, notably spectral bands 3.5-5.5 Hz and 0-1 Hz proved to be highly significant. The classification results for determination of tremor achieved 94% sensitivity with specificity equaling one.

A spirochete isolated from a case of severe virulent ovine foot disease is closely related to a Treponeme isolated from human periodontitis and bovine digital dermatitis

The isolation of spirochetes from severe ovine foot disease has been reported recently by our research group. In this study we describe the preliminary classification of this spirochete based on nucleotide sequence analysis of the PCR-amplified 16S rRNA gene. Phylogenetic analysis of this sequence in comparison with other previously reported 16S rRNA gene sequences showed that the spirochete belonged to the treponemal phylotype Treponema vincentii which has been associated with bovine digital dermatitis and human periodontal disease. Further work is required to define the common virulence determinants of these closely related treponemes in the aetiology of these tissue destructive diseases. (C) 2000 Elsevier Science B.V. All rights reserved.

Dietary patterns and cardiovascular disease

Despite strong prospective epidemiology and mechanistic evidence for the benefits of certain micronutrients in preventing CVD, neutral and negative outcomes from secondary intervention trials have undermined the efficacy of supplemental nutrition in preventing CVD. In contrast, evidence for the positive impact of specific diets in CVD prevention, such as the Dietary Approaches to Stop Hypertension (DASH) diet, has focused attention on the potential benefits of whole diets and specific dietary patterns. These patterns have been scored on the basis of current guidelines for the prevention of CVD, to provide a quantitative evaluation of the relationship between diet and disease. Using this approach, large prospective studies have reported reductions in CVD risk ranging from 10 to 60% in groups whose diets can be variously classified as 'Healthy', 'Prudent', Mediterranean' or 'DASH compliant'. Evaluation of the relationship between dietary score and risk biomarkers has also been informative with respect to underlying mechanisms. However, although this analysis may appear to validate whole-diet approaches to disease prevention, it must be remembered that the classification of dietary scores is based on current understanding of diet-disease relationships, which may be incomplete or erroneous. Of particular concern is the limited number of high-quality intervention studies of whole diets, which include disease endpoints as the primary outcome. The aims of this review are to highlight the limitations of dietary guidelines based on nutrient-specific data, and the persuasive evidence for the benefits of whole dietary patterns on CVD risk. It also makes a plea for more randomised controlled trials, which are designed to support food and whole dietary-based approaches for preventing CVD.

Advanced feature selection methods in multinominal dementia classification from structural MRI data

Recent studies showed that features extracted from brain MRIs can well discriminate Alzheimer’s disease from Mild Cognitive Impairment. This study provides an algorithm that sequentially applies advanced feature selection methods for findings the best subset of features in terms of binary classification accuracy. The classifiers that provided the highest accuracies, have been then used for solving a multi-class problem by the one-versus-one strategy. Although several approaches based on Regions of Interest (ROIs) extraction exist, the prediction power of features has not yet investigated by comparing filter and wrapper techniques. The findings of this work suggest that (i) the IntraCranial Volume (ICV) normalization can lead to overfitting and worst the accuracy prediction of test set and (ii) the combined use of a Random Forest-based filter with a Support Vector Machines-based wrapper, improves accuracy of binary classification.

An LDA and probability-based classifier for the diagnosis of Alzheimer's Disease from structural MRI

In this paper a custom classification algorithm based on linear discriminant analysis and probability-based weights is implemented and applied to the hippocampus measurements of structural magnetic resonance images from healthy subjects and Alzheimer’s Disease sufferers; and then attempts to diagnose them as accurately as possible. The classifier works by classifying each measurement of a hippocampal volume as healthy controlsized or Alzheimer’s Disease-sized, these new features are then weighted and used to classify the subject as a healthy control or suffering from Alzheimer’s Disease. The preliminary results obtained reach an accuracy of 85.8% and this is a similar accuracy to state-of-the-art methods such as a Naive Bayes classifier and a Support Vector Machine. An advantage of the method proposed in this paper over the aforementioned state of the art classifiers is the descriptive ability of the classifications it produces. The descriptive model can be of great help to aid a doctor in the diagnosis of Alzheimer’s Disease, or even further the understand of how Alzheimer’s Disease affects the hippocampus.