We describe the process of establishing a large database for the investigation of craniotomy infection and the preliminary results of this database. The initial results have been used to generate a cost analysis for craniotomy infection. The craniotomy infections database prospectively registers craniotomy cases taking place in the John Radcliffe Hospital. In order to achieve this, each patient's details are registered at the time of operation and followed up to identify cases of infection. Infection was defined strictly according to Centre for Disease Control criteria and validated by at least two members of clinical staff. The first 10 months of data are presented here which identifies a total of 245 craniotomies and 20 verified craniotomy infections. An overall infection rate of 8% is identified, and the cost incurred by the neurosurgery department as a result of craniotomy infections is estimated at £1 85 660 for the 10-month period studied. This amounts to a cost per case of infection of £9283.
We highlight the role of DBS in desynchronizing network activity, particularly in the high β band. The current study of GPi-DBS suggests these network-level effects are not necessarily dependent and potentially may be independent of the hyperdirect pathway. Importantly, these results draw a sharp distinction between the potential significance of low β oscillations locally within the basal ganglia and high β oscillations across the BGTC motor circuit.
BackgroundOver the years, a number of distinct treatments have been adopted for the management of the motor symptoms of Parkinson’s disease (PD), including pharmacologic therapies and deep brain stimulation (DBS). Efficacy is most often evaluated by subjective assessments, which are prone to error and dependent on the experience of the examiner. Our goal was to identify an objective means of assessing response to therapy.MethodsIn this study, we employed objective analyses in order to visualize and identify differences between three groups: healthy control (N = 10), subjects with PD treated with DBS (N = 12), and subjects with PD treated with levodopa (N = 16). Subjects were assessed during execution of three dynamic tasks (finger taps, finger to nose, supination and pronation) and a static task (extended arm with no active movement). Measurements were acquired with two pairs of inertial and electromyographic sensors. Feature extraction was applied to estimate the relevant information from the data after which the high-dimensional feature space was reduced to a two-dimensional space using the nonlinear Sammon’s map. Non-parametric analysis of variance was employed for the verification of relevant statistical differences among the groups (p < 0.05). In addition, K-fold cross-validation for discriminant analysis based on Gaussian Finite Mixture Modeling was employed for data classification.ResultsThe results showed visual and statistical differences for all groups and conditions (i.e., static and dynamic tasks). The employed methods were successful for the discrimination of the groups. Classification accuracy was 81 ± 6% (mean ± standard deviation) and 71 ± 8%, for training and test groups respectively.ConclusionsThis research showed the discrimination between healthy and diseased groups conditions. The methods were also able to discriminate individuals with PD treated with DBS and levodopa. These methods enable objective characterization and visualization of features extracted from inertial and electromyographic sensors for different groups.
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