Sleep is a complex yet organised process consisting of regular cycles of sleep stages. These stages are rapid-eye movement and non-rapid eye movement (light sleep and slow-wave sleep). Sleep staging is of great importance in the physiological world as sleep disorders occur in around 20% of the population and are associated with a multitude of serious health implications and considerable economic burden. Hidden Markov models have been successfully used in the classification of individual sleep stages measured by polysomnography, which is considered the ‘gold standard’ in assessing sleep, however, it is intrusive and costly.

Actigraphy is increasingly being considered as a non-intrusive and cost effective alternative method to objectively measure sleep patterns. However, there is limited research on the ability of actigraphy to detect individual sleep stages, furthermore, this research indicates that these current methodologies do not have the ability to do so. Current actigraphic approaches to sleep detection use filtered uni-axial data measured with a low sampling rate, whereas, raw tri-axial data measured at high sampling rates is frequently used in the assessment of day-time activities.

Using simultaneously measured actigraphy and polysomnography data from 100 healthy young adults in the Western Australian Pregnancy Cohort Study we created and validated an algorithm to determine sleep stages utilising raw, tri-axial acceleration data from wrist actigraphy. Ten feature variables were created from each 30-second block of data and 50 subjects were used to train the hidden Markov model with the feature variables used as input parameters. The remaining 50 subjects were used to validate the trained hidden Markov model against polysomnography.

Validation suggested that our model is able to classify sleep stages using raw tri-axial actigraphy data. These results demonstrate that actigraphy-based hidden Markov models can feasibly be used for automatic sleep staging.