  Parkinsons disease (PD) is a debilitating motor system disorder characterized
by progressive loss of movement, tremors, and speech slurring. PD is due to the
loss of dopamine-producing brain cells, and symptoms only worsen over time,
making early detection and diagnosis of the disease key to effective management
and treatment. There is currently no standardized method of diagnosis
available, and instead a combination of the medical history of a patient and
physician judgment is used. In this research, a novel method of accurately
diagnosing PD using an artificial neural network (ANN) and speech measurements
was developed. Using this technology, a patient need only speak into a computer
microphone. Speech data is then analyzed using Praat and inputted into the ANN
to obtain the diagnosis. The ANN, built using MATLAB and trained and tested
with actual patient data, was able to correctly classify 96.55% of test data. A
mathematical model of the GPe/STN neural cluster was then constructed to
account for Parkinsonian tremor. This was done using a two-cell model that
coupled a GPe neuron to a STN neuron and consisted of a system of twelve
ordinary differential equations. The model was structured to account for neuron
bursting behavior, neurotransmitter release, and synaptic connections between
neurons. After comparison to the biological behavior of the cluster and neuron
firing patterns in PD patients, the model was determined to be predictive of
known biological results. This work presents a significant step forward in PD
research and could be successfully implemented into clinical practice.
