Journal
JOURNAL OF NEUROSCIENCE METHODS
Volume 160, Issue 2, Pages 359-367Publisher
ELSEVIER
DOI: 10.1016/j.jneumeth.2006.09.020
Keywords
wavelet transform; spike detection; muscle sympathetic nerve activity; higher order statistics; humans
Categories
Funding
- NCRR NIH HHS [M01 RR000095, RR00095] Funding Source: Medline
- NHLBI NIH HHS [1P01 HL56693, P01 HL056693] Funding Source: Medline
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The accurate assessment of autonomic sympathetic function is important in the diagnosis and study of various autonomic and cardiovascular disorders. Sympathetic function in humans can be assessed by recording the muscle sympathetic nerve activity, which is characterized by synchronous neuronal discharges separated by periods of neural silence dominated by colored Gaussian noise. The raw nerve activity is generally rectified, integrated, and quantified using the integrated burst rate or area. We propose an alternative quantification involving spike detection using a two-stage stationary wavelet transform (SWT) de-noising method. The SWT coefficients are first separated into noise-related and burst-related coefficients on the basis of their local kurtosis. The noise-related coefficients are then used to establish a threshold to identify spikes within the bursts. This method demonstrated better detection performance than an unsupervised amplitude discriminator and similar wavelet-based methods when confronted with simulated data of varying burst rate and signal to noise ratio. Additional validation on data acquired during a graded head-up tilt protocol revealed a strong correlation between the mean spike rate and the mean integrate burst rate (r = 0.85) and burst area rate (r = 0.91). In conclusion, the kurtosis-based wavelet de-noising technique is a potentially useful method of studying sympathetic nerve activity in humans. (c) 2006 Elsevier B.V. All rights reserved.
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