Transitions in effective scaling behavior of accelerometric time series across sleep and wake

Wohlfahrt, Patrick; Kantelhardt, Jan W.; Zinkhan, Melanie; Schumann, Aicko Y.; Penzel, Thomas; Fietze, Ingo; Pillmann, Frank; Stang, Andreas

doi:10.1209/0295-5075/103/68002

Cited by 17 publications

(24 citation statements)

References 27 publications

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“…As the correlations agree with the variability one can assume with some confidence that high complexities (correlations around a ¼ 1)-in this case-are also indicative of a poor night's sleep. This is the first time such results have been shown since this is the first study that involves complexity analysis of actigraphy for the identification of acute insomnia; however, studies in the past have highlighted the merit of complexity analysis of actigraph measurements [24,25,37].…”

Section: Discussionmentioning

confidence: 84%

Complexity analysis of sleep and alterations with insomnia based on non-invasive techniques

Holloway

Angelova

Lombardo

et al. 2014

J. R. Soc. Interface.

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For the first time, fractal analysis techniques are implemented to study the correlations present in sleep actigraphy for individuals suffering from acute insomnia with comparisons made against healthy subjects. Analysis was carried out for 21 healthy individuals with no diagnosed sleep disorders and 26 subjects diagnosed with acute insomnia during night-time hours. Detrended fluctuation analysis was applied in order to look for 1/f-fluctuations indicative of high complexity. The aim is to investigate whether complexity analysis can differentiate between people who sleep normally and people who suffer from acute insomnia. We hypothesize that the complexity will be higher in subjects who suffer from acute insomnia owing to increased night-time arousals. This hypothesis, although contrary to much of the literature surrounding complexity in physiology, was found to be correct-for our study. The complexity results for nearly all of the subjects fell within a 1/f-range, indicating the presence of underlying control mechanisms. The subjects with acute insomnia displayed significantly higher correlations, confirmed by significance testingpossibly a result of too much activity in the underlying regulatory systems. Moreover, we found a linear relationship between complexity and variability, both of which increased with the onset of insomnia. Complexity analysis is very promising and could prove to be a useful non-invasive identifier for people who suffer from sleep disorders such as insomnia.

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Section: Discussionmentioning

confidence: 84%

Complexity analysis of sleep and alterations with insomnia based on non-invasive techniques

Holloway

Angelova

Lombardo

et al. 2014

J. R. Soc. Interface.

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“…While such summary measures allow for hypothesis testing using classic statistical methods [14,[21][22][23], large amounts of relevant information are disclosed in those complex data when more advanced tools of quantification (cf. Section 3), like fractal analysis, are applied [11,13,15,17,24,25]. In particular, actigraphy studies by Sun et al [15] indicated that the scaling exponent of the power law detected in temporal autocorrelation of activity significantly correlates with the severity of Parkinson's disease symptoms.…”

Section: Experiment: Actigraphy Recordingsmentioning

confidence: 99%

Seeking a fingerprint: analysis of point processes in actigraphy recording

Gudowska–Nowak¹,

Ochab²,

Oleś³

et al. 2016

J. Stat. Mech.

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Motor activity of humans displays complex temporal fluctuations which can be characterized by scale-invariant statistics, thus documenting that structure and fluctuations of such kinetics remain similar over a broad range of time scales. Former studies on humans regularly deprived of sleep or suffering from sleep disorders predicted change in the invariant scale parameters with respect to those representative for healthy subjects. In this study we investigate the signal patterns from actigraphy recordings by means of characteristic measures of fractional point processes. We analyse spontaneous locomotor activity of healthy individuals recorded during a week of regular sleep and a week of chronic partial sleep deprivation. Behavioural symptoms of lack of sleep can be evaluated by analysing statistics of duration times during active and resting states, and alteration of behavioural organization can be assessed by analysis of power laws detected in the event count distribution, distribution of waiting times between consecutive movements and detrended fluctuation analysis of recorded time series. We claim that among different measures characterizing complexity of the actigraphy recordings and their variations implied by chronic sleep distress, the exponents characterizing slopes of survival functions in resting states are the most effective biomarkers distinguishing between healthy and sleep-deprived groups.

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“…Our initial observation of a peak in the 0.3 Hz range (corresponding to breaths per minute) in nocturnal three-axis accelerometry data recorded at the wrist 32 was the starting point for our approach towards respiration proxies. After we had found that the high amplitude resolution of modern accelerometers (down to ) can resolve tiny motions caused by respiratory activity 10 , 32 , 33 , we have systematically studied if this effect can be used for a practical derivation of respiration proxies.…”

Section: Methodsmentioning

confidence: 99%

Reconstruction of the respiratory signal through ECG and wrist accelerometer data

Leube

Zschocke

Kluge

et al. 2020

Sci Rep

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Respiratory rate and changes in respiratory activity provide important markers of health and fitness. Assessing the breathing signal without direct respiratory sensors can be very helpful in large cohort studies and for screening purposes. In this paper, we demonstrate that long-term nocturnal acceleration measurements from the wrist yield significantly better respiration proxies than four standard approaches of ECG (electrocardiogram) derived respiration. We validate our approach by comparison with flow-derived respiration as standard reference signal, studying the full-night data of 223 subjects in a clinical sleep laboratory. Specifically, we find that phase synchronization indices between respiration proxies and the flow signal are large for five suggested acceleration-derived proxies with for males and for females (means ± standard deviations), while ECG-derived proxies yield only for males and for females. Similarly, respiratory rates can be determined more precisely by wrist-worn acceleration devices compared with a derivation from the ECG. As limitation we must mention that acceleration-derived respiration proxies are only available during episodes of non-physical activity (especially during sleep).

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Transitions in effective scaling behavior of accelerometric time series across sleep and wake

Cited by 17 publications

References 27 publications

Complexity analysis of sleep and alterations with insomnia based on non-invasive techniques

Complexity analysis of sleep and alterations with insomnia based on non-invasive techniques

Seeking a fingerprint: analysis of point processes in actigraphy recording

Reconstruction of the respiratory signal through ECG and wrist accelerometer data

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