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, through burst analysis and the examination of AP synchronicity within bursts, we validated our AP identification approach by demonstrating the correlation between APs and burst intervals