Журнал диагностических методов и биомедицинского анализа

Physiological Time Series: Analyse and Remove the Drift in 1-2-3

Aasen T and Nordahl SHG

A precondition for developing diagnostic tools based on analysing physiological time series is that there is pattern, i.e. information, in the data. Systematic variation in the data set is often masked by baseline drift and physiological fluctuations. In this study a simplest possible algorithm to eliminate the effect of drift and detect short-term pattern in physiological data is presented. The time series, xi, is filtered for the occurrences of three consecutive points with different values (xi ≠ xi+1 ≠ xi+2). Six different patterns are identified. The probability distribution of the pattern suggests if the time series is random or generated from a possible goal directed regulating activity. If the information entropy of the probability distribution of the pattern is close to one (not infinite long time series), the time series most likely come from a underlying process not offering qualitative information suitable for diagnostic purposes. The method is demonstrated on a data sequence generated from the logistic map in the infinite periodicity condition and a recorded optokinetic nystagmus amplitude time series.