Visual and computer-based detection of slow eye movements in overnight and 24-h EOG recordings.

OBJECTIVE

The present work aimed to evaluate the performance of an automatic slow eye movement (SEM) detector in overnight and 24-h electro-oculograms (EOG) including all sleep stages (1, 2, 3, 4, REM) and wakefulness.

METHODS

Ten overnight and five 24-h EOG recordings acquired in healthy subjects were inspected by three experts to score SEMs. Computerized EOG analysis to detect SEMs was performed on 30-s epochs using an algorithm based on EOG wavelet transform, recently developed by our group and initially validated by considering only pre-sleep wakefulness, stages 1 and 2.

RESULTS

The validation procedure showed the algorithm could identify epochs containing SEM activity (concordance index k=0.62, 80.7% sensitivity, 63% selectivity). In particular, the experts and the algorithm identified SEM epochs mainly in pre-sleep wakefulness, stage 1, stage 2 and REM sleep. In addition, the algorithm yielded consistent indications as to the duration and position of SEM events within the epoch.

CONCLUSIONS

The study confirmed SEM activity at physiological sleep onset (pre-sleep wakefulness, stage 1 and stage 2), and also identified SEMs in REM sleep. The algorithm proved reliable even in the stages not used for its training.

SIGNIFICANCE

The study may enhance our understanding of SEM meaning and function. The algorithm is a reliable tool for automatic SEM detection, overcoming the inconsistency of manual scoring and reducing the time taken by experts.