Detection of Parkinson’s disease tremor-onset with a frequency analysing artificial neural networkBurgess, J. G. (2008) Detection of Parkinson’s disease tremor-onset with a frequency analysing artificial neural network. In: SSE Systems Engineering Conference 2008, 25-26 Sep 2008, The University of Reading. (Unpublished)
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. Abstract/SummaryDeep Brain Stimulator devices are becoming widely used for therapeutic benefits in movement disorders such as Parkinson's disease. Prolonging the battery life span of such devices could dramatically reduce the risks and accumulative costs associated with surgical replacement. This paper demonstrates how an artificial neural network can be trained using pre-processing frequency analysis of deep brain electrode recordings to detect the onset of tremor in Parkinsonian patients. Implementing this solution into an 'intelligent' neurostimulator device will remove the need for continuous stimulation currently used, and open up the possibility of demand-driven stimulation. Such a methodology could potentially decrease the power consumption of a deep brain pulse generator.
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