Classifying vulnerability to sleep deprivation using baseline measures of psychomotor vigilance.

Objectives: To identify measures derived from baseline psychomotor vigilance task (PVT) performance that can reliably predict vulnerability to sleep deprivation.

Design: Subjects underwent total sleep deprivation and completed a 10-min PVT every 1-2 h in a controlled laboratory setting. Participants were categorized as vulnerable or resistant to sleep deprivation, based on a median split of lapses that occurred following sleep deprivation.

Improved patient specific seizure detection during pre-surgical evaluation.

Objective: There is considerable interest in improved off-line automated seizure detection methods that will decrease the workload of EEG monitoring units. Subject-specific approaches have been demonstrated to perform better than subject-independent ones. However, for pre-surgical diagnostics, the traditional method of obtaining a priori data to train subject-specific classifiers is not practical.

Towards a genioglossus surface EMG model of obstructive sleep apnea.

The behaviour and activity of the genioglossus muscle during sleep is of considerable interest to investigators of obstructive sleep apnea syndrome. Therefore, we contribute an model of genioglossus EMG activity during breathing, based on recent physiological findings. We present the modelling techniques and simulation results.