Automatic Epileptic Seizure Detection Using Scalp EEG and Advanced Artificial Intelligence Techniques

The epilepsies are a heterogeneous group of neurological disorders and syndromes characterised by recurrent, involuntary, paroxysmal seizure activity, which is often associated with a clinicoelectrical correlate on the electroencephalogram. The diagnosis of epilepsy is usually made by a neurologist but can be difficult to be made in the early stages. Supporting paraclinical evidence obtained from magnetic resonance imaging and electroencephalography may enable clinicians to make a diagnosis of epilepsy and investigate treatment earlier. However, electroencephalogram capture and interpretation are time consuming and can be expensive due to the need for trained specialists to perform the interpretation. Automated detection of correlates of seizure activity may be a solution. In this paper, we present a supervised machine learning approach that classifies seizure and nonseizure records using an open dataset containing 342 records. Our results show an improvement on existing studies by as much as 10% in most cases with a sensitivity of 93%, specificity of 94%, and area under the curve of 98% with a 6% global error using a k-class nearest neighbour classifier.We propose that such an approach could have clinical applications in the investigation of patients with suspected seizure disorders

A cross-sector analysis of human and organisational factors in the deployment of data-driven predictive maintenance

Domains such as utilities, power generation, manufacturing and transport are increasingly turning to data-driven tools for management and maintenance of key assets. Whole ecosystems of sensors and analytical tools can provide complex, predictive views of network asset performance. Much research in this area has looked at the technology to provide both sensing and analysis tools. The reality in the field, however, is that the deployment of these technologies can be problematic due to user issues, such as interpretation of data or embedding within processes, and organisational issues, such as business change to gain value from asset analysis. 13 experts from the field of remote condition monitoring, asset management and predictive analytics across multiple sectors were interviewed to ascertain their experience of supplying data-driven applications. The results of these interviews are summarised as a framework based on a predictive maintenance project lifecycle covering project motivations and conception, design and development, and operation. These results identified critical themes for success around having a target or decision-led, rather than data-led, approach to design; long-term resourcing of the deployment; the complexity of supply chains to provide data-driven solutions and the need to maintain knowledge across the supply chain; the importance of fostering technical competency in end-user organisations; and the importance of a maintenance-driven strategy in the deployment of data-driven asset management. Emerging from these themes are recommendations related to culture, delivery process, resourcing, supply chain collaboration and industry-wide cooperation

Smart garment for trunk posture monitoring: A preliminary study

© 2008 Wong and Wong; licensee BioMed Central Ltd

Life values as predictors of pain, disability and sick leave among Swedish registered nurses: a longitudinal study

<p>Abstract</p> <p>Background</p> <p>Prospective studies on high-risk populations, such as subgroups of health care staff, are limited, especially prospective studies among staff not on sick-leave. This paper is a report of a longitudinal study conducted to describe and compare the importance and consistency of life domains among registered nurses (RNs) working in a Swedish hospital and evaluate a model based on the consistency of valued life domains for prediction of pain, disability and sick leave.</p> <p>Method</p> <p>Importance and consistency ratings of life values, in 9 domains, were collected during 2003 and 2006 from 196 RNs using the Valued Living Questionnaire (VLQ). Logistic regression analyses were used for prediction of pain, disability and sick leave at the three-year follow-up. The predictors family relations, marriage couples/intimate relations, parenting, friends/social life, work, education, leisure time, psychological well-being, and physical self-care were used at baseline.</p> <p>Results</p> <p>RNs rated life values regarding parenting as most important and with the highest consistency both at baseline and at follow-up. No significant differences were found between RNs' ratings of importance and consistency over the three-year period, except for friends/social relations that revealed a significant decrease in importance at follow-up. The explanatory models for pain, disability and sick leave significantly predicted pain and disability at follow-up. The odds of having pain were significantly increased by one consistency rating (psychological well-being), while the odds were significantly decreased by physical self-care. In the model predicting disability, consistency in psychological well-being and education significantly increased the odds of being disabled, while consistency in physical self-care significantly decreased the odds.</p> <p>Conclusion</p> <p>The results suggest that there might be a link between intra-individual factors reflecting different aspects of appraised life values and musculoskeletal pain (MSP).</p

Variation in the provision and practice of implant-based breast reconstruction in the UK: Results from the iBRA national practice questionnaire

Introduction The introduction of biological and synthetic meshes has revolutionised the practice of implant-based breast reconstruction (IBBR) but evidence for effectiveness is lacking. The iBRA (implant Breast Reconstruction evAluation) study is a national trainee-led project that aims to explore the practice and outcomes of IBBR to inform the design of a future trial. We report the results of the iBRA National Practice Questionnaire (NPQ) which aimed to comprehensively describe the provision and practice of IBBR across the UK. Methods A questionnaire investigating local practice and service provision of IBBR developed by the iBRA Steering Group was completed by trainee and consultant leads at breast and plastic surgical units across the UK. Summary data for each survey item were calculated and variation between centres and overall provision of care examined. Results 81 units within 79 NHS-hospitals completed the questionnaire. Units offered a range of reconstructive techniques, with IBBR accounting for 70% (IQR:50–80%) of participating units' immediate procedures. Units on average were staffed by 2.5 breast surgeons (IQR:2.0–3.0) and 2.0 plastic surgeons (IQR:1.0–3.0) performing 35 IBBR cases per year (IQR:20-50). Variation was demonstrated in the provision of novel different techniques for IBBR especially the use of biological (n = 62) and synthetic (n = 25) meshes and in patient selection for these procedures. Conclusions The iBRA-NPQ has demonstrated marked variation in the provision and practice of IBBR in the UK. The prospective audit phase of the iBRA study will determine the safety and effectiveness of different approaches to IBBR and allow evidence-based best practice to be explored

Development and Validation of a Nurse Station Ergonomics Assessment Tool

Background: Nurse stations are one of the primary units for supporting effective functioning of any hospital. They are important working environments that demand adherence to known ergonomic principles for the well-being of both staff and patients. The aim of this study was to develop a psychometrically tested tool for the assessment of the ergonomic conditions of nurse workstations in hospitals. Methods: Ten hospitals, with a total of 133 nurse stations participated in this mixed-methods research. The domains and items of the tool were developed based on a literature review, an experts’ panel, and interviews with nurses. Results: The final nurse station ergonomic assessment (NSEA) tool has good psychometric properties. Validity was assessed by face validity and content validity. Reliability was evaluated using inter-rater agreement and test-retest reliability analyses with a four-week interval between assessments. The NSEA is comprised of 64 items across eight domains: layout and location (7 items), workspace (11 items), security-safety (5 items), environmental conditions (8 items), counter (8 items), chair (13 items), desk (9 items), and monitor (3 items). Conclusions: The NSEA adds to the literature a tool for managers to ensure they comply with legal requirements and support best practice for those working on hospital wards. The NSEA can be used to identify challenges for healthcare professionals who use nurse stations and support the execution of targeted interventions to improve human-environment interaction