Automatic sleep detection using activity and facial electrodes.

The most commonly applied unobtrusive sleep monitoring method is actigraphy, the measurement of body limb movements. In spite of its wide clinical acceptance, actigraphy has a low specificity for sleep detection leaving room for novel approaches of unobtrusive sleep monitoring. The present study compared sleep detection by a novel single channel electro-oculography (EOG) method and three activity monitors, with the golden standard of polysomnographic sleep analysis as a reference. With standard actigraphy (Actiwatch placed at the left wrist) sleep detection specificity and sensitivity were 42% and 95%. With the Alive Monitor attached on the same wrist, activity-based sleep detection specificity and sensitivity were 40% and 97%. With another Alive Monitor placed over the sternum sleep detection specificity and sensitivity were 21% and 99%. With two self-applied EOG electrodes combined with automatic sleep detection analysis, specificity and sensitivity were 72% and 96%. The results confirm low specificity of actigraphic sleep estimates, and demonstrate that the novel single-channel EOG method provides a substantial improvement in specificity.

Automatic sleep stage classification using two facial electrodes.

Standard sleep stage classification is based on visual analysis of central EEG, EOG and EMG signals. Automatic analysis with a reduced number of sensors has been studied as an easy alternative to the standard. In this study, a single-channel electro-oculography (EOG) algorithm was developed for separation of wakefulness, SREM, light sleep (S1, S2) and slow wave sleep (S3, S4). The algorithm was developed and tested with 296 subjects. Additional validation was performed on 16 subjects using a low weight single-channel Alive Monitor. In the validation study, subjects attached the disposable EOG electrodes themselves at home. In separating the four stages total agreement (and Cohen's Kappa) in the training data set was 74% (0.59), in the testing data set 73% (0.59) and in the validation data set 74% (0.59). Self-applicable electro-oculography with only two facial electrodes was found to provide reasonable sleep stage information.