Electroencephalogram approximate entropy influenced by both age and sleep

Abstract

The use of information-based measures to assess changes in conscious state is an increasingly popular topic. Though recent results have seemed to justify the merits of such methods, little has been done to investigate the applicability of such measures to children. For our work, we used the approximate entropy (ApEn), a measure previously shown to correlate with changes in conscious state when applied to the electroencephalogram (EEG), and sought to confirm whether previously reported trends in adult ApEn values across wake and sleep were present in children. Besides validating the prior findings that ApEn decreases from wake to sleep (including wake, rapid eye movement (REM) sleep, and non-REM sleep) in adults, we found that previously reported ApEn decreases across vigilance states in adults were also present in children (ApEn trends for both age groups: wake > REM sleep > non-REM sleep). When comparing ApEn values between age groups, adults had significantly larger ApEn values than children during wakefulness. After the application of an 8 Hz high-pass filter to the EEG signal, ApEn values were recalculated. The number of electrodes with significant vigilance state effects dropped from all 109 electrodes with the original 1 Hz filter to 1 electrode with the 8 Hz filter. The number of electrodes with significant age effects dropped from 10 to 4. Our results support the notion that ApEn can reliably distinguish between vigilance states, with low-frequency sleep-related oscillations implicated as the driver of changes between vigilance states. We suggest that the observed differences between adult and child ApEn values during wake may reflect differences in connectivity between age groups, a factor which may be important in the use of EEG to measure consciousness.

Changes in EEG multiscale entropy and power‐law frequency scaling during the human sleep cycle

“…The reduction of brain signal complexity with increasing sleep depth is in good agreement with previous findings in humans (Acharya et al, 2005;Bruce et al, 2009;Burioka et al, 2005;Lee et al, 2013;Nicolaou & Georgiou, 2011;Shi et al, 2017) and rodents (Abásolo et al, 2015). Our results extend this body of knowledge by measuring multiscale brain signal variability in contrast to the majority of previous studies that have quantified entropy/complexity at single time scale factors, without performing temporal coarse graining (except for Shi et al, 2017).…”

“…The overall trend reported by studies encompassing human and non-human animal models, is that signal diversity decreases from wakefulness to the NREM-1 and NREM-2 stages, reaching its nadir in slow-wave sleep (SWS), before recovering to near waking levels during REM epochs (Abásolo, Simons, Morgado da Silva, Tononi, & Vyazovskiy, 2015;Acharya, Faust, Kannathal, Chua, & Laxminarayan, 2005;Bruce, Bruce, & Vennelaganti, 2009;Burioka et al, 2005;Lee, Fattinger, Mouthon, Noirhomme, & Huber, 2013;Mateos, Guevara Erra, Wennberg, & Perez Velazquez, 2018;Nicolaou & Georgiou, 2011;Shi, Shang, Ma, Sun, & Yeh, 2017). The overall trend reported by studies encompassing human and non-human animal models, is that signal diversity decreases from wakefulness to the NREM-1 and NREM-2 stages, reaching its nadir in slow-wave sleep (SWS), before recovering to near waking levels during REM epochs (Abásolo, Simons, Morgado da Silva, Tononi, & Vyazovskiy, 2015;Acharya, Faust, Kannathal, Chua, & Laxminarayan, 2005;Bruce, Bruce, & Vennelaganti, 2009;Burioka et al, 2005;Lee, Fattinger, Mouthon, Noirhomme, & Huber, 2013;Mateos, Guevara Erra, Wennberg, & Perez Velazquez, 2018;Nicolaou & Georgiou, 2011;Shi, Shang, Ma, Sun, & Yeh, 2017).…”

“…A major gap stems from the scarce evidence about the degree to which entropy effects are driven by: (i) the linear properties of brain signals (reflected in the power spectrum), (ii) nonlinear time dependencies, or (iii) some combination of the two. The authors concluded that high frequency spectral components were entropy raising and lower frequencies entropy suppressing (see also Lee et al, 2013;Mizuno et al, 2010), and that overall, entropy reflects the balance between sleep and alertness promoting factors. One sleep study that directly explored this question found that single scale estimates of EEG entropy were strongly predicted by the logtransformed ratio of high (alpha and beta) to low frequency (delta and theta) spectral power (Bruce, Bruce, & Vennelaganti, 2009).…”

“…Accumulating evidence suggests that brain signal entropy tracks gradations in global consciousness. The overall trend reported by studies encompassing human and non-human animal models, is that signal diversity decreases from wakefulness to the NREM-1 and NREM-2 stages, reaching its nadir in slow-wave sleep (SWS), before recovering to near waking levels during REM epochs (Abásolo, Simons, Morgado da Silva, Tononi, & Vyazovskiy, 2015;Acharya, Faust, Kannathal, Chua, & Laxminarayan, 2005;Bruce, Bruce, & Vennelaganti, 2009;Burioka et al, 2005;Lee, Fattinger, Mouthon, Noirhomme, & Huber, 2013;Mateos, Guevara Erra, Wennberg, & Perez Velazquez, 2018;Nicolaou & Georgiou, 2011;Shi, Shang, Ma, Sun, & Yeh, 2017). Convergent findings point to reductions of neurophysiological signal complexity during the loss of consciousness induced by anesthesia (e.g., Ferenets, Vanluchene, Lipping, Heyse, & Struys, 2007;Schartner et al, 2015;Zhang, Roy, & Jensen, 2001) while hallucinogenic drugs produce a diversification of neuronal time series patterns consistent with their profound perceptual, cognitive, and emotional effects (e.g., Schartner, Carhart-Harris, Barrett, & Seth, 2017;Tagliazucchi, Carhart-Harris, Leech, Nutt, & Chialvo, 2014;Viol, Palhano-Fontes, Onias, de Araujo, & Viswanathan, 2017).…”

Design and optimisation of time-frequency analysis for multichannel neonatal EEG background features in term neonates with hypoxic ischaemic encephalopathy: characterisation, classification and neurodevelopmental outcome prediction

“…Studies also used entropy-based features such as approximate entropy (ApEn) [235,236] and sample entropy (SpEn) [237] with outcome.…”