Addressing the challenges of sleep/wake class imbalance in bed based non-contact actigraphic recordings of sleep.

Utilising strategically positioned bed-mounted accelerometers, the Passive Sleep Actigraphy platform aims to deliver a non-contact method for identifying periods of wakefulness during night-time sleep. One of the key problems in developing data driven approaches for automatic sleep monitoring is managing the inherent sleep/wake class imbalance. In the current study, actigraphy data from three participants over a period of 30 days was collected. Upon examination, it was found that only 10% contained wake data. Consequently, this resulted in classifier overfitting to the majority class (sleep), thereby impeding the ability of the Passive Sleep Actigraphy platform to correctly identify periods of wakefulness during sleep; a key measure in the identification of sleep problems. Utilising Spread Subsample and Synthetic Minority Oversampling Techniques, this paper demonstrates a potential solution to this issue, reporting improvements of up to 28% in wake detection when compared to baseline data while maintaining an overall classifier accuracy of 90%.