Automatic glissade determination through a mathematical model in electrooculographic records
This work addresses a specific issue in ocular movement analysis for medical or research applications, but it appears incremental as it builds on existing methods for glissade determination.
The paper tackled the problem of automatically detecting glissadic overshoots in electrooculographic records by developing a procedure that uses a mathematical model and machine learning, achieving expected responses for presence detection.
The glissadic overshoot is characterized by an unwanted type of movement known as glissades. The glissades are a short ocular movement that describe the failure of the neural programming of saccades to move the eyes in order to reach a specific target. In this paper we develop a procedure to determine if a specific saccade have a glissade appended to the end of it. The use of the third partial sum of the Gauss series as mathematical model, a comparison between some specific parameters and the RMSE error are the steps made to reach this goal. Finally a machine learning algorithm is trained, returning expected responses of the presence or not of this kind of ocular movement.