An Improved Method for Estimating Human Circadian Phase Derived From Multichannel Ambulatory Monitoring and Artificial Neural Networks

Abstract

Recently, we developed a novel method for estimating human circadian phase with noninvasive ambulatory measurements combined with subject-independent multiple regression models and a curve-fitting approach. With this, we were able to estimate circadian phase under real-life conditions with low subject burden, i.e., without need of constant routine (CR) laboratory conditions, and without measuring standard circadian markers, such as core body temperature (CBT) or pineal hormone melatonin rhythms. The precision of ambulatory-derived estimated circadian phase was within an error of 12 ± 41 min (mean ± SD) in comparison to melatonin phase during a CR protocol. The physiological measures could be reduced to a triple combination: skin temperatures, irradiance in the blue spectral band of ambient light, and motion acceleration. Here, we present a nonlinear regression model approach based on artificial neural networks for a larger data set (25 healthy young males), including both the original data and additional data collected in the same protocol and using the same equipment. Throughout our validation study, subjects wore multichannel ambulatory monitoring devices and went about their daily routine for 1 wk. The devices collected a large number of physiological, behavioral, and environmental variables, including CBT, skin temperatures, cardiovascular and respiratory functions, movement/posture, ambient temperature, spectral composition and intensity of light perceived at eye level, and sleep logs. After the ambulatory phase, study volunteers underwent a 32-h CR protocol in the laboratory for measuring unmasked circadian phase (i.e., "midpoint" of the nighttime melatonin rhythm). To overcome the complex masking effects of many different confounding variables during ambulatory measurements, neural network-based nonlinear regression techniques were applied in combination with the cross-validation approach to subject-independent prediction of circadian phase. The most accurate estimate of circadian phase with a prediction error of -3 ± 23 min (mean ± SD) was achieved using only two types of the measured variables: skin temperatures and irradiance for ambient light in the blue spectral band. Compared to our previous linear multiple regression modeling approach, motion acceleration data can be excluded and prediction accuracy, nevertheless, improved. Neural network regression showed statistically significant improvement of variance of prediction error over traditional approaches in determining circadian phase based on single predictors (CBT, motion acceleration, or sleep logs), even though none of these variables was included as predictor. We, therefore, have identified two sets of noninvasive measures that, combined with the prediction model, can provide researchers and clinicians with a precise measure of internal time, in spite of the masking effects of daily behavior. This method, here validated in healthy young men, requires testing in a clinical or shiftwork population suffering from circadian sleep-wake disorders.

Human Circadian Phase Estimation from Signals Collected in Ambulatory Conditions Using an Autoregressive Model

“…Skin temperature has been shown to possess a circadian rhythm (Krauchi and Wirz-Justice, 1994;Krauchi et al, 2000) and has also been used in a linear regression model (Kolodyazhniy et al, 2011) and a nonlinear neural network model (Kolodyazhniy et al, 2012) to estimate circadian phase. Nevertheless, when considering other signals, emphasis must be given to the use of signals collected noninvasively, in ambulatory conditions, and for periods of time in the order of 24 hours.…”

“…Some commonly used signals are activity level, light exposure Kronauer et al, 1999;St. Hilaire et al, 2007b;Mott et al, 2011), and skin temperature (Kolodyazhniy et al, 2011(Kolodyazhniy et al, , 2012. The drawback with these methods is that they require the collection of data over extended periods of time, in some cases up to 1 week, to obtain accurate results.…”

The Characterization of Biological Rhythms in Mild Cognitive Impairment

“…Furthermore, internal desynchrony among the different rhythms could also account for these inconsistencies. As a result, reliable evaluation through multichannel ambulatory monitoring of the status of the circadian system is beginning to receive attention [12, 13]. A better understanding of the changes in this system would aid in the implementation of prevention therapies, such as controlled bright light therapy and/or melatonin administration, which have already proven their relevance in these pathologies [14].…”

To study non linear features in circadian heart rate variability amongst healthy subjects

“…Researchers have presented the study of human circadian rhythms by developing constant routine protocols to assess the status of the circadian pacemaker in constant behavioral and environmental conditions [6 ]. Recently, researchers developed a new method for estimating human circadian phase with noninvasive ambulatory measurements combined with subject independent multiple regression models and a curve-fitting approach [7].…”