Related Experiment Video
Updated: Jul 16, 2026
Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content
Published on: May 26, 2018
Visual and computer-based detection of slow eye movements in overnight and 24-h EOG recordings
E Magosso1, M Ursino, A Zaniboni
1Department of Electronics, Computer Science and Systems, University of Bologna, Viale Risorgimento 2, I-40136 Bologna, Italy. emagosso@deis.unibo.it
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.
