Fig. 1. Detection method of synchronized sympathetic nerve activity.
Sympathetic nerve activity (SNA) recorded in multifiber nerve preparation shows a grouped burst discharges, which is defined as synchronized SNA. The synchronized SNA is detected by a computerized peak detection algorithm (Cluster program) [Malpas S. C. et al, Am. J. Physiol., 263: H1311-H1317, 1992]. However the recorded SNA often contains noise consisting of ECG and base-line drift due to spontaneous movement of the body. Therefore, it is necessary to detect more accurately the synchronized SNA with a high signal-to-noise ratio. This study presents a new method developed for detection of synchronized SNA.
The detection algorithm consists of three signal processing techniques which involve: (1) extracting an approximate region containing synchronized sympathetic nerve activity, (2) separating individual neural signals from recorded compound nerve activity and (3) detecting the onset and end of each synchronized neural signals. By this process, the algorithm can precisely detect synchronized sympathetic nerve activity.
The ECG (a) and original SNA (b) recorded from the renal nerve in anesthetized cats were shown. The recorded SNA was rectified (c) and integrated by a RC integrator with a time constant of 20 ms (d). The original SNA and the integrated SNA were digitized at a sampling rate of 8 kHz using a 12 bit digital converter and stored on a personal computer. The cluster program scans the integrated SNA data series for a significant increase followed by significant decreases in a small cluster of voltage values and detects the peak (open circles in (d)) and pre- and postpeak nadirs(open squares in (d)). The synchronized SNA approximate region is defined as between prepeak nadir and postpeak nadir (e).
Detection of individual neural signals from the original signal (b) is accomplished by using a statistical method. The large amplitude signals reflect the synchronized neural signals. On the other hand, the small amplitude signals in the neighborhood of the baseline are regarded as either small nonsynchronized neural discharges or noise. To obtain a statistical difference between the large and small amplitude signals, a statistic is caluculated with the variances of each signals (c). When the statics has a significant difference by a Chi square test (significant level = 0.05), then the large amplitude signal having large variance is identified as a SNA (d).
Differentiation of the onset and end from the approximate region of the SNA is accomplished by using the detected pre- and postpeak nadirs (c) and individual neural signals (d). The positions of onset and end are given by at top and end positions of the neural signal detected within the approximate region of the SNA, respectively. The synchronized SNA(SSNA) is detected(e).
The widths measured by the new statistic method ranged from 20 to 280 ms were smaller than that measured by previous Cluster method ranged from 60 to 300ms and reflect the duration of large amplitude of SNA.
The SNA recorded from the multifiber preparation is a continuously fluctuating variable in terms of period, amplitude and width, reflecting a coordinated tonic level of output from the vasomotor center. Therefore analysis of these variables found in discharges of sympathetic nerve is essential for understanding the central organization of the autonomic nervous system. However current methods of analysis can not precisely detect these variables, especially at width. The developed algorithm can determine the onset and end of synchronized SNA and accurately detect the width. This method could be applied to many types of sympathetic nerves that exhibit synchronized discharges.