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. (Author correspondence: [email protected])

Keywords:

• Ambient light
• Cross-validation
• Curve fitting
• Human circadian rhythms
• Melatonin
• Multichannel ambulatory recording
• Multilayer perceptron
• Skin temperatures

ACKNOWLEDGMENTS

We thank our collaborators, Prof. Domien Beersma from the University of Groningen for his valuable input on the design of the study protocol; Drs. Anand Kumar and Cees Lijzenga from the Personal Health Institute International (Phi-I) who designed the ClockWatcher; Luzian Wolf from Object Tracker who attended to LightWatcher development and improvement; Claudia Renz, Marie-France Dattler, and Giovanni Balestrieri from the Centre for Chronobiology for their help in data acquisition; Marielle Kappeler and all staff who took care of the constant routine in the chronobiology laboratory at the Psychiatric Hospital of the University of Basel, Basel, Switzerland.

Declaration of Interest: This study was supported by the EU FP6 integrated project 018741 EUCLOCK (www.euclock.eu). The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.