Spike detection of human sympathetic nerve activity using wavelet transformation and Valsalva maneuver denoising

Sympathetic function is directly assessed by microneurography measuring muscle sympathetic nerve activity (MSNA). The recordings are typically corrupted with noise and require denoising. We aim to estimate microneurographic noise individually from physiologically suppressed MSNA during Valsalva phase 4 (VM4).

We developed MSNA adaptive processing (MAP). MSNA recordings during Valsalva were transformed by stationary wavelet transformation. Level-specific noise thresholds were computed from 4 SD of detail coefficients from VM4 and were implemented for denoising. The denoised signals were inverse transformed, then the MSNA spikes were detected. We compared detection performance of the MAP with the current two-stage kurtosis method in simulated MSNA signals, and recordings from 17 healthy and 19 postural orthostatic tachycardia syndrome (POTS) female subjects performing Valsalva.

The MAP had higher correct detections of MSNA spikes than the kurtosis method in simulated signals wit high burst rate (50 burst/min) and low signal-to-noise ratio (SNR =2) (MAP vs kurtosis; 23.81 ± 15.49 % vs 16.98 ± 12.75 %, p < 0.001). The improvement was confirmed by shorter error distance of the precision-recall plot (0.535 ± 0.175 vs 0.542 ± 0.177, p = 0.011). The MAP detected higher spike rate during VM phase 2 in healthy (24.11 ± 9.85 vs 19.57 ± 8.60 spike/s, p = 0.049), but non-significant in POTS (24.19 ± 13.70 vs 20.30 ± 11.85 spike/s, p = 0.101).

The detection performance of the MAP is superior to the current two-stage kurtosis method.

The proposed MAP method individually estimating noise from VM4 could improve MSNA spike detection, compared with the kurtosis method. The advantages are most prominent in high burst rate and low SNR MSNA recordings.