Complexity of Body Movements during Sleep in Children with Autism Spectrum Disorder

Abstract

Recently, measuring the complexity of body movements during sleep has been proven as an objective biomarker of various psychiatric disorders. Although sleep problems are common in children with autism spectrum disorder (ASD) and might exacerbate ASD symptoms, their objectivity as a biomarker remains to be established. Therefore, details of body movement complexity during sleep as estimated by actigraphy were investigated in typically developing (TD) children and in children with ASD. Several complexity analyses were applied to raw and thresholded data of actigraphy from 17 TD children and 17 children with ASD. Determinism, irregularity and unpredictability, and long-range temporal correlation were examined respectively using the false nearest neighbor (FNN) algorithm, information-theoretic analyses, and detrended fluctuation analysis (DFA). Although the FNN algorithm did not reveal determinism in body movements, surrogate analyses identified the influence of nonlinear processes on the irregularity and long-range temporal correlation of body movements. Additionally, the irregularity and unpredictability of body movements measured by expanded sample entropy were significantly lower in ASD than in TD children up to two hours after sleep onset and at approximately six hours after sleep onset. This difference was found especially for the high-irregularity period. Through this study, we characterized details of the complexity of body movements during sleep and demonstrated the group difference of body movement complexity across TD children and children with ASD. Complexity analyses of body movements during sleep have provided valuable insights into sleep profiles. Body movement complexity might be useful as a biomarker for ASD.

Parameter Analysis of Multiscale Two-Dimensional Fuzzy and Dispersion Entropy Measures Using Machine Learning Classification

“…Information theory, relative entropy, and the Kullback-Leibler divergence are now widely used concepts (see, e.g., References [1][2][3]). Entropy-based algorithms have enabled engineers and researchers to measure the uncertainty and irregularity of complex systems and data [4][5][6]. The corresponding algorithms have become a key tool in many application areas, particularly in the biomedical domain.…”

General spectral characteristics of human activity and its inherent scale-free fluctuations

“…Even though actigraphic recordings are usually several days long, the fluctuation functions of activity signals in the relevant studies are generally evaluated over timescales ranging only from a few minutes to some hours 29 , 47 – 49 , while activity signals recorded during sleep 33 , 34 , 50 and wakefulness 51 , 52 are typically analysed separately. Although these DFA-based studies identified power-law scaling, they were typically limited to a given activity type and to assess how different diseases (i.e., Alzheimer’s disease 51 , Klein-Levin disease 47 , depression 53 , bipolar disorder 54 , autism spectrum disorder 34 ) or even aging 55 break down the patterns compared to control groups without giving a detailed description of the general fluctuation patterns of human activity. Regarding frequency-domain analysis, some studies 6 , 56 , 57 have already noted in the case of two given types of activity signals (up to 1-week-long time series, examined over their entire length) that they contain 1/ f fluctuations above the frequency of the daily rhythmicity without giving further details on the general spectral characteristic.…”

“…In conclusion, previous studies have already partially examined the scale-free nature of human activity by analysing fluctuation functions of activity signals for box widths of less than 24 h typically separating into sleep and wakefulness for medical purposes 29 , 33 , 34 , 47 – 52 , PSDs of two given types of several-day-long activity signals over the entire frequency range 6 , 56 , 57 , and fluctuations of acceleration signals both with DFA and PSD (using Welch’s method) over a narrower timescale and frequency range, respectively 46 . …”

“…fluctuation functions of activity signals for box widths of less than 24 h typically separating into sleep and wakefulness for medical purposes 29 , 33 , 34 , 47 – 52 ,…”

Active Learning in Robotics: A Review of Control Principles

“…Recent work analyzing animal and human movement has begun to interpret physical bodies as information channels and motions as informationcarrying signals. This has led to the development of methods that help to understand the pathology of conditions such as autism spectrum disorder [21], schizophrenia [22], and stroke [23,24] through an information-theoretic analysis of movement. More generally, this suggests that in order to realize learning objectives, active learning requires measures that capture the information content of an agent's movements.…”