In this work we devise a classification of @entity19 activity patterns based on accelerometer data using Detrended Fluctuation Analysis. We use two characteristic @entity19 behavioural states as benchmarks in this study: waking in free activity and @entity25 ( @entity25 ). In both situations we find roughly the same pattern: for short time intervals we observe high correlation in activity - a typical 1/f complex pattern - while for large time intervals there is anti-correlation. High correlation of short intervals ([Formula: see text] to [Formula: see text]: waking state and [Formula: see text] to [Formula: see text]: @entity25 ) is related to highly coordinated muscle activity. In the waking state we associate high correlation both to muscle activity and to @entity19 stereotyped movements (grooming, waking, etc.). On the other side, the observed anti-correlation over large time scales ([Formula: see text] to [Formula: see text]: waking state and [Formula: see text] to [Formula: see text]: @entity25 ) during @entity25 appears related to a feedback autonomic response. The transition from correlated regime at short scales to an anti-correlated regime at large scales during @entity25 is given by the respiratory cycle interval, while during the waking state this transition occurs at the time scale corresponding to the duration of the stereotyped @entity19 movements. Furthermore, we find that the waking state is characterized by longer time scales than @entity25 and by a softer transition from correlation to anti-correlation. Moreover, this soft transition in the waking state encompass a behavioural time scale window that gives rise to a multifractal pattern. We believe that the observed multifractality in @entity19 activity is formed by the integration of several stereotyped movements each one with a characteristic time correlation. Finally, we compare scaling properties of body acceleration fluctuation time series during sleep and wake periods for healthy @entity19 . Interestingly, differences between sleep and wake in the scaling exponents are comparable to previous works regarding @entity1 heartbeat. Complementarily, the nature of these sleep-wake dynamics could lead to a better understanding of neuroautonomic regulation mechanisms.

XXXX Activity across Time Scales: Fractal Scenarios.

@entity19 :: ('10090', 'Species') :: ['mice', 'mouse'] @entity1 :: ('9606', 'Species') :: ['human'] @entity25 :: ('MESH:D012893', 'Disease') :: ['slow-wave sleep', 'SWS']
@entity19 :: (10090,Species) :: ['Mouse']
