Authors:
Filippo Cona
1
;
Fabio Pizza
2
;
Federica Provini
2
and
Elisa Magosso
1
Affiliations:
1
University of Bologna, Italy
;
2
University of Bologna and Bellaria Hospital, Italy
Keyword(s):
Slow Eye Movements, Sleep Onset, Automatic Detection, Template Matching.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Supervised and Unsupervised Learning
;
Theory and Methods
Abstract:
An algorithm that can automatically identify slow eye movements from the electro-oculogram is presented. The automatic procedure is trained using the visual classification of an expert scorer. The algorithm makes use of both the spectral and morphological signal information to detect single slow eye movements. On the basis of this detection some parameters that characterize the slow eye movements (amplitude, duration, velocity and number) are extracted. A few possible applications of the algorithm are shown by means of a preliminary study: the average patterns of slow eye movements parameters at sleep onset are evaluated for healthy volunteers and for patients affected by obstructive sleep apnea syndrome. Finally, general considerations are drawn regarding the clinical interest of the study.