Identification of pulses in hormone time series using outlier detection methods.
The identification of discrete hormonal secretory pulses is of critical importance in clinical endocrinology. Pulses are defined as sudden increases in hormone concentration followed by exponential decay. We propose a model-based iterative procedure for pulse detection in pulsatile hormone time series. Our model is seen to be analogous to the model for innovation outliers in autoregressive series, and outlier detection techniques for pulse identification are adapted to the endocrine context. An original feature of the procedure is that it distinguishes between true pulses and gross observation outliers in the series. Simulation experiments are used to investigate the behaviour of the method under physiologically or clinically relevant circumstances. Five experimental endocrine series from rhesus monkeys, where the times of the pulses are known from the concomitant recording of the electrical activity of the hypothalamus, are analysed.