Human Circadian Phase Estimation using Unobtrusive Wearable Sensor Data

Abstract of the Project

Phase estimation of the human endogenous circadian rhythm has been explored using various modeling approaches. This work focuses on the estimation of circadian phase using heart rate variability (HRV), both in the time and the frequency domain. Given the HRV signal’s proneness to masking effects, careful consideration will be given to the demasking of this signal and the selection of robust modeling techniques. Statistically trained models will be developed to arrive at an accurate estimate of the endogenous circadian phase of a person in ambulatory conditions.  Other circadian signals (beyond HRV) which can be measured non-invasively will be explored.  This will lead to a more practical, inexpensive, and less time consuming way of assessing the state of the circadian clock, which can then be used (among others) for the optimal deployment of chronotherapeutics.

Research Achievements to date

•  Data collection carried out on 25 participants.  A two-week protocol was designed where actigraphy, light exposure, skin temperature, electrocardiogram, and evening melatonin was collected.
• Attendance and poster presentation at the international Chronobiology Summer School in Berlin 2012. 
• Submitted article to the Journal of Biological Rhythms “Human circadian phase estimation from signals collected in ambulatory conditions using an autoregressive model”

References

[1] Gil EA, Aubert XL, Møst EIS, and Beersma DGM. Human circadian phase estimation from signals collected in ambulatory conditions using an autoregressive model.  

[2] St Hilaire MA, Klerman EB, Khalsa SBS, Wright KP Jr, Czeisler CA and Kronauer RE (2007) Addition of a non-photic component to a light-based mathematical model of the human circadian pacemaker. Journal of theoretical biology, 247(4), 583–599.

[3] Kolodyazhniy V, Späti J, Frey S, Götz T, Wirz-Justice A, Kräuchi K, Cajochen C and Wilhelm FH (2011) Estimation of Human Circadian Phase via a Multi-Channel Ambulatory Monitoring System and a Multiple Regression Model. Journal of Biological Rhythms, 26(1), 55–67.

[4] Kolodyazhniy V, Späti J, Frey S, Götz T, Wirz-Justice A, Kräuchi K, Cajochen C and Wilhelm FH (2012) An improved method for estimating human circadian phase derived from multichannel ambulatory monitoring and artificial neural networks. Chronobiology international, 29(8), 1078–1097.