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Parkinsons disease (PD) is a debilitating motor system disorder characterizedby progressive loss of movement, tremors, and speech slurring. PD is due to theloss of dopamine-producing brain cells, and symptoms only worsen over time,making early detection and diagnosis of the disease key to effective managementand treatment. There is currently no standardized method of diagnosisavailable, and instead a combination of the medical history of a patient andphysician judgment is used. In this research, a novel method of accuratelydiagnosing PD using an artificial neural network (ANN) and speech measurementswas developed. Using this technology, a patient need only speak into a computermicrophone. Speech data is then analyzed using Praat and inputted into the ANNto obtain the diagnosis. The ANN, built using MATLAB and trained and testedwith actual patient data, was able to correctly classify 96.55% of test data. Amathematical model of the GPe/STN neural cluster was then constructed toaccount for Parkinsonian tremor. This was done using a two-cell model thatcoupled a GPe neuron to a STN neuron and consisted of a system of twelveordinary differential equations. The model was structured to account for neuronbursting behavior, neurotransmitter release, and synaptic connections betweenneurons. After comparison to the biological behavior of the cluster and neuronfiring patterns in PD patients, the model was determined to be predictive ofknown biological results. This work presents a significant step forward in PDresearch and could be successfully implemented into clinical practice.

Mathematically Modeling the GPe/STN Neuronal Cluster to Account for  Parkinsonian Tremor and Developing a Novel Method to Accurately Diagnose  Parkinson's Disease Using Speech Measurements and an Artificial Neural  Network