The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform
L. Litewka - One of the best experts on this subject based on the ideXlab platform.
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Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study.
The Lancet. Neurology, 2013Co-Authors: Mark J. Cook, Terence J. O'brien, Samuel F. Berkovic, Michael Murphy, Andrew P. Morokoff, Gavin Fabinyi, Wendyl D'souza, Raju Yerra, John S. Archer, L. LitewkaAbstract:Summary Background Seizure prediction would be clinically useful in patients with epilepsy and could improve safety, increase independence, and allow acute treatment. We did a multicentre clinical feasibility study to assess the safety and efficacy of a long-term implanted seizure advisory system designed to predict seizure likelihood and quantify seizures in adults with drug-resistant focal seizures. Methods We enrolled patients at three centres in Melbourne, Australia, between March 24, 2010, and June 21, 2011. Eligible patients had between two and 12 disabling partial-onset seizures per month, a lateralised epileptogenic zone, and no history of psychogenic seizures. After devices were surgically implanted, patients entered a Data Collection Phase, during which an algorithm for identification of periods of high, moderate, and low seizure likelihood was established. If the algorithm met performance criteria (ie, sensitivity of high-likelihood warnings greater than 65% and performance better than expected through chance prediction of randomly occurring events), patients then entered an advisory Phase and received information about seizure likelihood. The primary endpoint was the number of device-related adverse events at 4 months after implantation. Our secondary endpoints were algorithm performance at the end of the Data Collection Phase, clinical effectiveness (measures of anxiety, depression, seizure severity, and quality of life) 4 months after iniation of the advisory Phase, and longer-term adverse events. This trial is registered with ClinicalTrials.gov, number NCT01043406. Findings We implanted 15 patients with the advisory system. 11 device-related adverse events were noted within four months of implantation, two of which were serious (device migration, seroma); an additional two serious adverse events occurred during the first year after implantation (device-related infection, device site reaction), but were resolved without further complication. The device met enabling criteria in 11 patients upon completion of the Data Collection Phase, with high likelihood performance estimate sensitivities ranging from 65% to 100%. Three patients' algorithms did not meet performance criteria and one patient required device removal because of an adverse event before sufficient training Data were acquired. We detected no significant changes in clinical effectiveness measures between baseline and 4 months after implantation. Interpretation This study showed that intracranial electroencephalographic monitoring is feasible in ambulatory patients with drug-resistant epilepsy. If these findings are replicated in larger, longer studies, accurate definition of preictal electrical activity might improve understanding of seizure generation and eventually lead to new management strategies. Funding NeuroVista.
Hazel Watson - One of the best experts on this subject based on the ideXlab platform.
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Data collecting in grounded theory--some practical issues.
Nurse researcher, 2004Co-Authors: Kathleen Duffy, Colette Ferguson, Hazel WatsonAbstract:In this paper, Kathleen Duffy, Colette Ferguson and Hazel Watson discuss the challenges of using grounded theory methodology in research, particularly when used for the first time. With reference to a study of the factors influencing mentors' decisions when student nurses' clinical performance is unsatisfactory, they highlight some of the practical issues relevant to the Data Collection Phase of the research process.
Mark J. Cook - One of the best experts on this subject based on the ideXlab platform.
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Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study.
The Lancet. Neurology, 2013Co-Authors: Mark J. Cook, Terence J. O'brien, Samuel F. Berkovic, Michael Murphy, Andrew P. Morokoff, Gavin Fabinyi, Wendyl D'souza, Raju Yerra, John S. Archer, L. LitewkaAbstract:Summary Background Seizure prediction would be clinically useful in patients with epilepsy and could improve safety, increase independence, and allow acute treatment. We did a multicentre clinical feasibility study to assess the safety and efficacy of a long-term implanted seizure advisory system designed to predict seizure likelihood and quantify seizures in adults with drug-resistant focal seizures. Methods We enrolled patients at three centres in Melbourne, Australia, between March 24, 2010, and June 21, 2011. Eligible patients had between two and 12 disabling partial-onset seizures per month, a lateralised epileptogenic zone, and no history of psychogenic seizures. After devices were surgically implanted, patients entered a Data Collection Phase, during which an algorithm for identification of periods of high, moderate, and low seizure likelihood was established. If the algorithm met performance criteria (ie, sensitivity of high-likelihood warnings greater than 65% and performance better than expected through chance prediction of randomly occurring events), patients then entered an advisory Phase and received information about seizure likelihood. The primary endpoint was the number of device-related adverse events at 4 months after implantation. Our secondary endpoints were algorithm performance at the end of the Data Collection Phase, clinical effectiveness (measures of anxiety, depression, seizure severity, and quality of life) 4 months after iniation of the advisory Phase, and longer-term adverse events. This trial is registered with ClinicalTrials.gov, number NCT01043406. Findings We implanted 15 patients with the advisory system. 11 device-related adverse events were noted within four months of implantation, two of which were serious (device migration, seroma); an additional two serious adverse events occurred during the first year after implantation (device-related infection, device site reaction), but were resolved without further complication. The device met enabling criteria in 11 patients upon completion of the Data Collection Phase, with high likelihood performance estimate sensitivities ranging from 65% to 100%. Three patients' algorithms did not meet performance criteria and one patient required device removal because of an adverse event before sufficient training Data were acquired. We detected no significant changes in clinical effectiveness measures between baseline and 4 months after implantation. Interpretation This study showed that intracranial electroencephalographic monitoring is feasible in ambulatory patients with drug-resistant epilepsy. If these findings are replicated in larger, longer studies, accurate definition of preictal electrical activity might improve understanding of seizure generation and eventually lead to new management strategies. Funding NeuroVista.
Yancheng Sui - One of the best experts on this subject based on the ideXlab platform.
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An Energy-Aware and Void-Avoidable Routing Protocol for Underwater Sensor Networks
IEEE Access, 2018Co-Authors: Zhuo Wang, Guangjie Han, Hongde Qin, Suping Zhang, Yancheng SuiAbstract:Underwater sensor networks are facing a great challenge in designing a routing protocol with longer network lifetime and higher packet delivery rate under the complex underwater environment. In this paper, we propose an energy-aware and void-avoidable routing protocol (EAVARP). EAVARP includes layering Phase and Data Collection Phase. During the layering Phase, concentric shells are built around sink node, and sensor nodes are distributed on different shells. Sink node performs hierarchical tasks periodically to ensure the validity and real-time of the topology. It makes EAVARP apply to dynamic network environment. During the Data Collection Phase, Data packets are forwarded based on different concentric shells through opportunistic directional forwarding strategy (ODFS), even if there are voids. The ODFS takes into account the remaining energy and Data transmission of nodes in the same shell, and avoids cyclic transmission, flooding, and voids. The verification and analysis of simulation results show the effectiveness of our proposed EAVARP in terms of selecting performance matrics in comparison to existing routing protocols.
Michele Lamb - One of the best experts on this subject based on the ideXlab platform.
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Human Rights and Social Policy: Challenges and Opportunities for Social Research and Its Use as Evidence in the Protection and Promotion of Human Rights in Scotland
Social Policy and Society, 2012Co-Authors: Alison C.s. Hosie, Michele LambAbstract:This article is the result of a joint project conducted by University of Roehampton and the Scottish Human Rights Commission, undertaken between March 2010 and April 2011. The article describes the methodological challenges encountered in the Data Collection Phase of the project and addresses the conceptual relationship between social science and human rights research. It argues that social policy and social sciences research has an important contribution to make in the promotion and protection of human rights and demonstrates the use of the Research in Scotland's National Action Plan for Human Rights, being prepared by the Commission during 2012–13.
