Detection and classification of raw action potential patterns in human Muscle Sympathetic Nerve Activity.
The Muscle Sympathetic Nerve Activity (MSNA) consists of synchronous neural discharges separated by periods of neural silence dominated by heavy background noise. During measurement with electrodes, the raw MSNA signal is amplified, band-pass filtered, rectified and integrated. This integration process removes much neurophysiological information. In this paper a method for detecting a raw action potential (before the pre-amplifier) and filtered action potential (after the band-pass filter) is presented. This method is based on stationary wavelet transform (SWT) and a peak detection algorithm. Also, the detected action potentials were clustered using the k-means method and the cluster averages were calculated. The action potential detector and classification algorithm are evaluated using real MSNA recorded from three healthy subjects.
Salmanpour
Aryan
A
Department of Electrical and Computer Engineering, and the Neurovascular Research Laboratory, the School of Kinesiology, the University of Western Ontario, ON, Canada. asalmanp@uwo.ca
Brown
Lyndon J
LJ
Shoemaker
J K
JK
D000200:Action Potentials / Q000502:physiology*; D000328:Adult; D000465:Algorithms; D004566:Electrodes; D004594:Electrophysiology / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological; D009132:Muscles / Q000294:innervation*; D009474:Neurons / Q000473:pathology; D009482:Neurophysiology / Q000379:methods; D010543:Peroneal Nerve / Q000473:pathology*; D012815:Signal Processing, Computer-Assisted; D013564:Sympathetic Nervous System / Q000473:pathology*