Heart Rate Variability During a Joint Attention Task in Toddlers With Autism Spectrum Disorders

Abstract

Background: Autism Spectrum Disorders (ASD) are a heterogeneous group of neurodevelopmental disorders featuring early impairments in social domain, with autonomic nervous system (ANS) unbalance possibly representing a useful marker for such disturbances. Impairments in joint attention (JA) are one of the earliest markers of social deficits in ASD. In this study, we assessed the feasibility of using wearable technologies for characterizing the ANS response in ASD toddlers during the presentation of JA stimuli.Methods: Twenty ASD toddlers and 20 age- and gender-matched typically developed (TD) children were recorded at baseline and during a JA task through an unobtrusive chest strap for electrocardiography (ECG). Specific algorithms for feature extraction, including Heart Rate (HR), Standard Deviation of the Normal-to-Normal Intervals (SDNN), Coefficient of Variation (CV), pNN10 as well as low frequency (LF) and high frequency (HF), were applied to the ECG signal and a statistical comparison between the two groups was performed.Results: As regards the single phases, SDNN (p = 0.04) and CV (p = 0.021) were increased in ASD at baseline together with increased LF absolute power (p = 0.034). Moreover, CV remained higher in ASD during the task (p = 0.03). Considering the phase and group interaction, LF increased from baseline to task in TD group (p = 0.04) while it decreased in the ASD group (p = 0.04).Conclusions: The results of this study indicate the feasibility of characterizing the ANS response in ASD toddlers through a minimally obtrusive tool. Our analysis showed an increased SDNN and CV in toddlers with ASD particularly at baseline compared to TD and lower LF during the task. These findings could suggest the possibility of using the proposed approach for evaluating physiological correlates of JA response in young children with ASD.

Wearable Sensors to Characterize the Autonomic Nervous System Correlates of Food-Like Odors Perception: A Pilot Study

“…Importantly, the use of wearable device, allow to record autonomic activity in an ecological contest. This new prospect has made physiological research accessible also outside the laboratory and allowed, for the first time, to study the autonomic response also in vulnerable subjects like people with mental health problems [67] or young children [46].…”

“…ECG signal was analyzed through a Matlab (Mathworks, Natick, MA, USA)-based interface properly developed, which made it possible to calculate the associated tachogram (RR series, i.e., the time elapsed between two successive R-waves) according to the Pan-Tompkins algorithm [43] and to extract both time-and frequency-domain features characterizing the ANS [44][45][46][47]. Pre-processing steps before computing the tachogram included an infinite impulse response (IIR) low pass filter at 40 Hz, which was applied to eliminate muscular noise, a stepwise filtering to remove artifacts and interferences, a cubic spline third order interpolation between the fiducial isoelectric points of the ECG to remove body movements and respiration, and a notch filter to remove the power line interference at 50 Hz.…”

How to Use Heart Rate Variability: Quantification of Vagal Activity in Toddlers and Adults in Long-Term ECG

“…Nevertheless, it is justifiable to set the upper frequency limit to 1.04 Hz, and the higher limit may provide additional information in the daytime. By contrast, our results indicate that the proposed bottom frequency limit of 0.24 Hz for children [ 35 , 36 , 37 ] may result in a loss of information content in toddlers, particularly in recordings in the daytime, although its physiological origin is still under debate [ 52 ]. Rather unexpectedly, the suggested adjustment of the HF band to the respiratory frequency [ 21 , 53 ] might result in information loss as well, even if dynamic adjustment within the individual is implemented, as can be seen in Table 2 .…”

“…Power spectral density (PSD) estimates were calculated for the 180 s segments via Burg’s method (model order 24) after removing the trend (2nd order). Given the two different age groups and the respective different recommendations in the literature, the high-frequency component (HF) of the heart rate variability was calculated for multiple frequency bands: HF1: 0.15–0.40 Hz, the recommended frequency band for adults [ 20 ] HF2: 0.15–0.80 Hz: frequency band proposed for children by Alkon et al [ 33 , 34 ] HF3: 0.24–1.04 Hz: further frequency band proposed for children [ 35 , 36 , 37 ] HF4: 0.15–1.04 Hz: the combination of the proposed frequency bands for children in terms of the maximal range Dynamically adjusted frequency bands based on the median respiration rate within the 180 s segment with: HF5: a minimum frequency of 0.15 Hz and a range of 0.25 Hz, equal to the band width of HF1 (see Figure 1 , please). The optimal frequency range was calculated based on the overlapping frequency range of HF1 and HF3, that is, the range from 0.24–0.40 Hz (center 0.32 Hz), and the difference of the current respiration frequency (in the same 180 s segment) from this center frequency of 0.32 Hz.…”

Can Machine Learning Predict Stress Reduction Based on Wearable Sensors’ Data Following Relaxation at Workplace? A Pilot Study

“…The interface also allows one to extract common time-and frequency-domain features associated with the signal [17,18], including:…”