Graduation Semester and Year

2011

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Biomedical Engineering

Department

Bioengineering

First Advisor

Jung-Chih Chiao

Abstract

Chronic pain is an important public health issue. Several approaches have been implemented for management of chronic pain, including surgical implantation of neurostimulators. Neurostimulators have been used in clinics for spinal cord implantation and they have been suitable for relieving certain types of pains, such as neuropathic pain in leg or arm, complex regional pain, and refractory angina. The stimulation at spinal cord has little relieving effects on syndromes such as facial pain, cluster headache, phantom limb pain, and post stroke pain. For treating such syndromes, electrical stimulation of deep brain structures such as thalamic nuclei (e.g. ventroposterolateral (VPL) or ventroposteromedial (VPM)), periaqueductal gray (PAG), periventricular gray (PVG), anterior cingulate cortex (ACC) and other regions near the central gray has been clinically suggested. After implantation of neurostimulators in the spinal cord or brain, physicians adjust the stimulation parameters based on the patients' verbal description of pain at the time or their own judgment of the pain suppression effect during the trial period. Doctors do not have a way to physiologically document the pain signals in a quantitative way. Hence, from the hardware perspective, the currently available neurostimulators perform in an open-loop fashion. This type of open-loop, continuously-operating stimulators are not adaptive and do not consider continuous neural feedback from the patient. Therefore, they are not always effective, and can give rise to stimulation-induced side effects. In contrast, several researchers have proposed the need for a closed-loop real-time system in neurostimulation to overcome these problems. A closed-loop feedback approach can provide a higher efficiency in terms of reduction in battery power consumption that will allow the implant to stay longer time in the patient's body, and reduction of side effects or syndromes such as excitotoxicity, leading to apoptosis. In order to maximize the desired outcomes, we designed and developed wireless systems and indices that can detect the nociception automatically based on neurological signals. The developed wireless system for acquiring ECoG signals included a front-end that could be worn by small laboratory animals, and a back-end that could be connected to a computer and interface with the user through a graphical user interface (GUI). The front-end included an analog based and a 2.4 Ghz transceiver (nRF24Le1, Nordic Semmiconductor) that included an analog to digital converter (ADC), a microcontroller and a transceiver. Furthermore, the platform was utilized to develop new applications of wireless technology for acquiring transcranial motor evoked potentials (TcMEP) and slow wave gastric electrical activity. These two systems were characterized and evaluated on animal models. We utilized extracellular single unit action potential signals from wide dynamic range (WDR) neurons in dorsal horn spinal cord and thalamus to detect nociception and make a closed-loop system. In addition, we have exploited electrocorticography (ECoG) in somatosensories and motor cortices to establish indices for objective detection of pain. Results showed that the WDR neurons in the spinal cord can be used to differentiate between graded mechanical stimuli, while WDR neurons in the thalamus can only be used to differentiate between the low and high intensity mechanical stimuli. Investigation of ECoG signals in rodent models showed that sharp pain caused by thermal stimulus leaves a peak signature in the time-domain signal and a difference in both Delta and Gamma band frequencies while; the dull pain caused by chemical stimulus only increases the Gamma frequency bands.WDR neurons in the spinal cord were utilized to develop a closed-loop feedback system that could acquire single-unit extracellular neuronal signals from the dorsal horn spinal cord in real-time and distinguish between the graded mechanical stimuli, i.e. brush (non painful), pressure (border between non-painful and painful) and pinch (painful). After setting, the system could automatically distinguish between the painful and non-painful signals and generated electrical stimulation during the painful signals. The indirect evidence of the decrease in the mean rate of the action potentials suggested that the system was able to inhibit the nociceptive signals.I conducted the majority of the work in this dissertation and received minor assistance from other students including Christopher E. Hagains, Philip G. McCorkle, Timothy W. Wiggins Jennifer L. Seifert, Ramin M. Askari, Shariq M. Athar, Greg O'grady and Leo K. Cheng to whom I am grateful and have acknowledged their contribution in my manuscripts which I first-authored. However, all the results and figures (except Figs. 1.1 and 3.1) in this dissertation are original and generated by me and they are not published in any other manuscript, dissertation or thesis.

Disciplines

Biomedical Engineering and Bioengineering | Engineering

Comments

Degree granted by The University of Texas at Arlington

Download

Share

COinS