A New Signal Processing Approach to Study Action Potential Content in Sympathetic Neural Signals
Bibliographic record
Abstract
Sympathetic nerve activity plays an essential role in the normal regulation of blood pressure in humans and in the etiology and progression of many chronic diseases. Sympathetic nerve recordings associated with blood pressure regulation can be recorded directly using microneurography. A general characteristic of this signal is spontaneous burst activity of spikes (action potentials) separated by silent periods against a background of considerable gaussian noise. During measurement with electrodes, the raw muscle sympathetic nerve activity (MSNA) signal is amplified, band-pass filtered, rectified and integrated. This integration process removes important information regarding action potential content and their discharge properties.\nThe first objective of this thesis was to propose a new method for detecting action potentials from the raw MSNA signal to enable investigation of post-ganglionic neural discharge properties. The new method is based on the design of a mother wavelet that is matched to an actual mean action potential template extracted from a raw MSNA signal and applying it to the raw MSNA signal using a continues wavelet transform (CWT) for spike detection. The performance of the proposed method versus two previous wavelet-based approaches was evaluated using 1) MSNA recorded from seven healthy participants and, 2) simulated MSNA. The results show that the new matched wavelet performs better than the previous wavelet-based methods that use a non-matched wavelet in detecting action potentials in the MSNA signal.\nThe second objective of this thesis was to employ the proposed action potential detection and classification technique to study the relationship between the recruitment of sympathetic action potentials (i.e., neurons) and the size of integrated sympathetic bursts in human MSNA signal. While in other neural systems (e.g. the skeletal motor system) there is a well understood pattern of neural recruitment during activation, our understanding of how sympathetic neurons are coordinated during baseline and baroreceptor unloading are very limited. We demonstrate that there exists a hierarchical pattern of recruitment of additional faster conducting neurons of larger amplitude as the sympathetic bursts become stronger. This information has important implications for how blood pressure is controlled, and the malleability of sympathetic activation in health and disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".