Instrumentation for diagnosis, clinical assessment, and treatment of motion disorders

Parkinson's Disease and Essential Tremor are among a broad class of diseases characterized as motion disorders and are among the most debilitating diseases known. Parkinson's Disease affects 1.5 million people in the United States alone, with over 60,000 new cases diagnosed each year; a related movement disorder, Essential Tremor, affects upwards of 10 million people.

In the past, physicians have evaluated human motion disorder diseases based on subjective observations of the patients and frequently relied on patient questionnaires. It would be significantly more desirable to provide mechanisms to quantify the effects of these diseases, giving medical professionals a means of accurately and precisely assessing the efficacy of treatments and confirming clinical diagnoses.

This thesis presents development of wearable data acquisition technology aimed especially for the needs of motion disorder researchers and clinicians. Emphasis is placed on designing for low power consumption (to maximize portability) and small size (to maximize wearability), while making maximum utilization of readily available components. In addition, accelerometer designs are considered using both analog and digital devices.

Deep brain stimulators are becoming an important part of the treatment regimen for tremor related disorders. An impediment to their use is a requirement that the patient be awake during insertion of the probe. Neurological researchers are studying electroencephalogram signals in response to signals from the stimulators in an effort that may lead to possible surgical techniques under general anesthesia. This thesis presents design and implementation of instrumentation for use during deep brain stimulator iv implantation to develop trigger signals for use with conventional electroencephalogram recording equipment. The design includes consideration for low-level analog amplification, digital signal processing, and galvanic isolation techniques.

Sensors suitable for long-term wearability require the use of intelligent data processing directly on the sensing circuitry. Of particular concern is a measure of motion symmetry. Previously, cross correlation techniques have been employed, but they are computationally expensive to perform. This thesis presents a sign correlation technique that shows promise in reducing computational complexity requirements, as well as a possible 16-fold reduction in memory requirements. Efficacy of the technique is demonstrated for both tremor and gait data.

Note: Abstract extracted from PDF file via OCR.

Digitization of this thesis was made possible by a generous grant from the Jefferson Trust, 2015.

Thesis originally deposited on 2016-03-17 in version 1.28 of Libra. This thesis was migrated to Libra2 on 2017-03-23 16:36:25.