Enhanced biological signal processing : integrating advanced frequency analysis and wavelet techniques for action potential detection in rats’ sympathetic nerve recordings
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Rajaei, Arman
issued: 2024-04-29T15:59:48Z
issued: 2024
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: This thesis explores the analysis of neuronal action potentials (AP) within sympathetic nerve activity (SNA) from rats' splanchnic nerves. Chosen for their physiological resemblance to humans, rats serve as a viable model for this research. We introduce an advanced AP detection algorithm employing wavelet transforms (WT), addressing limitations observed in previous methodologies. A primary focus is the identification of the noise floor in the recorded data, a pivotal step for distinguishing neural activity from background noise. Analyzing rats’ SNA, allows for the integration of pharmacological interventions to induce ganglionic blockades, which lead to the significant reduction or elimination of APs. By comparing pre- and post-blockade recordings, we establish a baseline threshold, enhancing our capability to identify true neural signals. Our new algorithm consists of two steps. In the first step, we have used the maximal overlap discrete wavelet transform (MODWT) to identify the timestamps of APs in the raw data. Since MODWT employs multiresolution analysis and each level acts as a bandpass filter, choosing the appropriate level of decomposition is important. Therefore, by incorporating frequency and spectrum analysis of APs, we have determined the appropriate decomposition level that overlaps with the frequency components of the APs. In the next step, we constructed a mother wavelet from the average of extracted APs for use in continuous wavelet transform (CWT). This approach likely yields more AP extractions compared to using predefined wavelets from the Symlet and Daubechies families. By iterating this process with the APs extracted from CWT, we refine our template, enabling its adaptation as a mother wavelet and aiding in the accurate determination of the shape and length of rat APs. Although Shafer et al., (2022) also refined their template, they initiated it from large APs, potentially introducing a bias towards larger spikes in their analysis. Additionally,…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/88057
note: Enhanced Biological Signal Processing: Integrating Advanced Frequency Analysis and Wavelet Techniques for Action Potential Detection in Rats’ Sympathetic Nerve Recordings by Arman Rajaei B.A., Shiraz University, 2015 MASc Shiraz University, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Mechanical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) April 2024 © Arman Rajaei, 2024 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis entitled: Enhanced Biological Signal Processing: Integrating Advanced Frequency Analysis and Wavelet Techniques for Action Potential Detection in Rats’ Sympathetic Nerve Recordings submitted by Arman Rajaei in partial fulfillment of the requirements of the degree of Master of Applied Science Dr. Rudolf Seethaler, School of Engineering, Supervisor Dr. Klaske van Heusden, School of Engineering Co-Supervisor Dr. Christopher R West, University of British Columbia-Vancouver Supervisory Committee Member Dr. Guy Dumont, University of British Columbia-Vancouver Supervisory Committee Member Dr. Christopher Collier, School of Engineering University Examiner iii Abstract This thesis explores the analysis of neuronal action potentials (AP) within sympathetic nerve activity (SNA) from rats' splanchnic nerves. Chosen for their physiological resemblance to humans, rats serve as a viable model for this research. We introduce an advanced AP detection algorithm employing wavelet transforms (WT), addressing limitations observed in previous methodologies. A primary focus is the identification of the noise floor in the recorded data, a pivotal step for distinguishing neural activity from background noise. Analyzing rats’ SNA, allows for the integration of pharmacological interventions to induce ganglionic blockades, which lead to the significant reduction or elimination of APs…
has type: Thesis/Dissertation
date issued: 2024-05
is shown at: 10.14288/1.0442028
language: eng
degree discipline: Mechanical Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nd/4.0/
scholar level: Graduate
contributor: Seethaler, Rudolf
contributor: van Heusden, Klaske
type: Text
identifier URI: http://hdl.handle.net/2429/88057