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

Automated link analysis using radio frequency identification (RFID)

Being able to accurately record the interactions which take place within any environment is beneficial for understanding human behaviour in a wide range of industries. Link Analysis is a standard technique which is often used, but traditional pen and paper methods are cumbersome and time consuming. This paper details a way to automate recording the interactions between a human and their current environment by using radio frequency identification (RFID) tags and a subject-mounted receiver. Using the results from the system, it is possible to instantly create conventional Link Analysis diagrams and tables, reducing the time and resources required for data collection and analysis. The system has been developed in partnership with the Healthcare Ergonomics and Patient Safety Unit (HEPSU) at Loughborough University, with initial focus being on monitoring paramedics, patients and environment interactions within an ambulance; however, the technologies and the analyser system are not limited to use within this particular field

Human factors for dementia: Evidence based design

Designing care environments for people living with dementia is a complex challenge as the key stakeholder may have difficulty communicating their capabilities, limitations and preferences. This paper describes the use of evidence-based design personas in a multi-disciplinary team with architects and chartered human factors specialists. Four individual personas (Alison, Barry, Christine and David) and a couple persona (Chris and Sally) were used to bring the voices of the people living with different stages of dementia to the design process. Their changing/fluctuating symptoms were communicated in two formats (wheel and matrix) within an inclusive design process to adapt a Victorian semi-detached house. The demonstrator house presents evidence based design, adaptation and support solutions to support people living with dementia to age well at home

Aerosol Characteristics at a High Altitude Location in Central Himalayas: Optical Properties and Radiative Forcing

Collocated measurements of the mass concentrations of aerosol black carbon (BC) and composite aerosols near the surface were carried out along with spectral aerosol optical depths (AODs) from a high altitude station, Manora Peak in Central Himalayas, during a comprehensive aerosol field campaign in December 2004. Despite being a pristine location in the Shivalik Ranges of Central Himalayas, and having a monthly mean AOD (at 500 nm) of 0.059 $\pm$ 0.033 (typical to this site), total suspended particulate (TSP) concentration was in the range 15 - 40 micro g m^(-3) (mean value 27.1 $\pm$ 8.3 micro g m^(-3)). Interestingly, aerosol BC had a mean concentration of 1.36 $\pm$ 0.99 micro g m^(-3), contributed to ~5.0 $\pm$ 1.3 % to the composite aerosol mass. This large abundance of BC is found to have linkages to the human activities in the adjoining valley and to the boundary layer dynamics. Consequently, the inferred single scattering albedo lies in the range of 0.87 to 0.94 (mean value 0.90 $\pm$ 0.03), indicating significant aerosol absorption. The estimated aerosol radiative forcing was as low as 4.2 W m^(-2) at the surface, +0.7 W m^(-2) at the top of the atmosphere, implying an atmospheric forcing of +4.9 W m^(-2). Though absolute value of the atmospheric forcing is quite small, which arises primarily from the very low AOD (or the column abundance of aerosols), the forcing efficiency (forcing per unit optical depth) was $\sim$88 W m^(-2), which is attributed to the high BC mass fraction.Comment: 32 Pages, Accepted in JGR (Atmosphere

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

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

Smart garment for trunk posture monitoring: A preliminary study

© 2008 Wong and Wong; licensee BioMed Central Ltd

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

Intervention mapping for development of a participatory return-to-work intervention for temporary agency workers and unemployed workers sick-listed due to musculoskeletal disorders

BACKGROUND: In the past decade in activities aiming at return-to-work (RTW), there has been a growing awareness to change the focus from sickness and work disability to recovery and work ability. To date, this process in occupational health care (OHC) has mainly been directed towards employees. However, within the working population there are two vulnerable groups: temporary agency workers and unemployed workers, since they have no workplace/employer to return to, when sick-listed. For this group there is a need for tailored RTW strategies and interventions. Therefore, this paper aims to describe the structured and stepwise process of development, implementation and evaluation of a theory- and practise-based participatory RTW program for temporary agency workers and unemployed workers, sick-listed due to musculoskeletal disorders (MSD). This program is based on the already developed and cost-effective RTW program for employees, sick-listed due to low back pain. METHODS: The Intervention Mapping (IM) protocol was used to develop a tailor-made RTW program for temporary agency workers and unemployed workers, sick-listed due to MSD. The Attitude-Social influence-self-Efficacy (ASE) model was used as a theoretical framework for determinants of behaviour regarding RTW of the sick-listed worker and development of the intervention. To ensure participation and facilitate successful adoption and implementation, important stakeholders were involved in all steps of program development and implementation. Results of semi-structured interviews and 'fine-tuning' meetings were used to design the final participatory RTW program. RESULTS: A structured stepwise RTW program was developed, aimed at making a consensus-based RTW implementation plan. The new program starts with identifying obstacles for RTW, followed by a brainstorm session in which the sick-listed worker and the labour expert of the Social Security Agency (SSA) formulate solutions/possibilities for suitable (therapeutic) work. This process is guided by an independent RTW coordinator to achieve consensus. Based on the resulting RTW implementation plan, to create an actual RTW perspective, a vocational rehabilitation agency is assigned to find a matching (therapeutic) workplace. The cost-effectiveness of this participatory RTW program will be evaluated in a randomised controlled trial. CONCLUSION: IM is a promising tool for the development of tailor-made OHC interventions for the vulnerable working populatio