SELF-ORGANIZED SEGMENTATION OF TIME-SERIES - SEPARATING GROWTH-HORMONE SECRETION IN ACROMEGALY FROM NORMAL CONTROLS

The pulsatile pattern of growth hormone (GH) secretion was assessed by sampling blood every 10 min over 24 h in healthy subjects (n = 10) un der normal food intake and under fasting conditions (n = 6) and in pat ients with a GH-producing tumor (acromegaly, n = 6), before and after treatment with the somatostatin analog octreotide. Using autocorrelati on, we found no consistent separation in the temporal dynamics of GH s ecretion in healthy controls and acromegalic patients. Time series pre diction based on a single neural network has recently been demonstrate d to separate the secretory dynamics of parathyroid hormone in healthy controls from osteoporotic patients. To better distinguish the differ ences in GH dynamics in healthy subjects and patients, we tested time series predictions based on a single neural network and a more refined system of multiple neural networks acting in parallel (adaptive mixtu res of local experts). Both approaches significantly separated GH dyna mics under the various conditions. By performing a self-organized segm entation of the alternating phases of secretory bursts and quiescence of GH, we significantly improved the performance of the multiple netwo rk system over that of the single network. It thus may represent a pot ential tool for characterizing alterations of the dynamic regulation a ssociated with diseased states.