Strong Correlation of Sleep Onset between EOG and EEG Sleep Stage 1 and 2

Abstract

Bus driversi¦ sleepiness on wheels would seriously threaten public safety. By manually analyzing electroencephalogram (EEG) changes to determine sleep onset latency, Maintenance of Wakefulness Test (MWT) is a gold standard to evaluate subjecti¦s ability to keep alert in twilight and quite situation. However, this test is not eligible to be applied to drivers at driving to avoid risk ahead. Therefore, for crucial and practical sakes, we tried to develop an auto-program, analyzing sole electro oculogram (EOG) signals in a quasi-real-time manner, to warn immediate working individuals when sense ongoing of sleep onset. Doubtlessly the computed sleep onset latency by this program, using indices of Blink and of slow eye movements (SEM), should agree to those by manual EEG judgment like MWT. Accordingly, 43 bus drivers were recruited in present study to test this hypothesis. No matter at first and third or second and fourth bouts, had each volunteer randomly received two bouts of quiet staying (traditional MWT testing) and rest two of LED light response testing with simultaneous EEG and EOG measures. While blink signals were characterized by pulse-type, high amplitude and high frequency (0.6-1.3 Hz), corresponding to fast eye closing and opening, SEM appeared low frequency (0.2iV0.6 Hz), corresponding to rolling, horizontal, bidirectional and conjugate eye movements. Sleep onset would be determined by the Blink index lower than a certain arbitrary threshold concomitant with the SEM index higher than another one, both threshold-settings fulfilling the situations of MWT-determined sleep-onset. After two indicesi¦ thresholds readjusted, the values of agreement and of sensitivity reached to 91.2% and 93.3%, respectively. In conclusion, with good agreement to MWT in terms of sleep onset latency, our innovated EOG-based program might be eligible to apply drivers at work for detect sleep onset in an almost real time manner.