Title:
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On demand DBS for Parkinson's Disease : tremor prediction using artificial neural networks
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In this thesis results are presented which relate to using artificial neural networks
to predict the onset of Parkinson's disease tremors in human subjects. Data for the
networks was obtained from implanted deep brain electrodes in human subjects. A
tuned artificial neural network was shown to be able to identify the pattern of the
onset tremor from these real time recordings.
Parkinson's disease (PO) is one disease in a group of conditions called movement
disorders. One of the primary symptoms of Parkinson's disease is tremor, and in
the extreme case, the patient can suffer loss of physical movement. There are two
major types of treatment for PO currently available, namely chemical treatment
(Levodopa) and surgical implants (Deep Brain Stimulation). Deep Brain
Stimulation (DBS) has been widely accepted as an efficient treatment for PO over
the past decade. Despite the high cost of surgical operation, deep brain
stimulation has become a widely accepted alternative choice (if not the only) to
medical treatment such as Levodopa for patients. In this work, number of methods
have been applied on exploring the possibility of determining PO tremor onset
from patient's brain signal, in particular using combination of artificial neural
networks (ANN) and advanced signal processing algorithms. The result of this
work could eventually lead to design a deep brain stimulation device with the
ability to react on different brain activities, for example, start stimulation just before
Parkinson's disease tremor onset. The benefits of such smart device are pre-Iong
DBS battery life and reduce stimulation interference on normal brain functions.
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