#Scilab identify outlier how to
We further comment on how to diagnose possible outliers from the series of IPIs which is the main output of the algorithm. We then describe the algorithm in details and perform it on different sets of both synthetic and experimental LH time series. We focus on explaining how the process of sampling affects drastically the original pattern of secretion, and especially the amplitude of the detectable pulses. We make use of synthetic data to make clear some basic notions underlying our algorithmic choices. In this article, we present an algorithm for the monitoring of LH pulse frequency, basing ourselves both on the available endocrinological knowledge on LH pulse (shape and duration with respect to the frequency regime) and synthetic LH data generated by a simple model. Hence, there is a real need for robust IPI detection algorithms. Yet, reliable information on the InterPulse Intervals (IPI) is a prerequisite to study precisely the steroid feedback exerted on the pituitary level. As a result, the pattern of plasma LH may be not so clearly pulsatile. Besides, experimental sampling occurs at a relatively low frequency (typically every 10 min) with respect to LH highest frequency release (one pulse per hour) and the resulting LH measurements are noised by both experimental and assay errors. Simultaneous measurements comparing LH levels in the cavernous sinus and jugular blood have revealed clear differences in the pulse shape, the amplitude and the baseline.
However, plasma levels result from a convolution process due to clearance effects when LH enters the general circulation. Whereas measurements in the cavernous sinus cumulate anatomical and technical difficulties, LH levels can be easily assessed from jugular blood. The endocrine control of the reproductive function is often studied from the analysis of luteinizing hormone (LH) pulsatile secretion by the pituitary gland.
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