This paper presents an actigraphy-based approach for sleep/wake detection for insomniacs. Due to its relative unobtrusiveness, actigraphy is often used to estimate overnight sleep-wake patterns in clinical practice. However, its performance has been shown to be limited in subjects with sleep complaints such as insomniacs. Quantifying activity counts on 30-s epoch basis, as usually done in regular actigraphy, may lead to an underestimation of wake periods where the subject shows reduced body movements. We therefore propose a new actigraphic feature to characterize the ‘possibility’ of epochs being asleep (or awake) before or after its nearest epoch with a very high activity levels. It is expected to correctly identify some wake epochs when they are very close to the high activity epochs, although they can be motionless. A data set containing 25 insomnia subjects and a linear discriminant classifier were used to test our approach in this study. Leave-one-subject-out cross validation results show that combining the new and the traditional actigraphic features led to a markedly improved performance in sleep/wake detection compared to that using the traditional feature only, with an increase in Cohen’s kappa from 0.49 to 0.55.
|Title of host publication||14th International Conference on Wearable and Implantable Body Sensor Networks (BSN'17), 9-12 May 2017, Eindhoven, The Netherlands|
|Place of Publication||Piscataway|
|Publisher||Institute of Electrical and Electronics Engineers|
|Number of pages||4|
|Publication status||Published - 1 Jun 2017|