Detection of normal and slow saccades using implicit piecewise polynomial approximation

Weiwei Dai, Ivan Selesnick, John Ross Rizzo, Janet Rucker, Todd Hudson

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

The quantitative analysis of saccades in eye movement data unveils information associated with intention, cognition, and health status. Abnormally slow saccades are indicative of neurological disorders and often imply a specific pathological disturbance. However, conventional saccade detection algorithms are not designed to detect slow saccades, and are correspondingly unreliable when saccades are unusually slow. In this article, we propose an algorithm that is effective for the detection of both normal and slow saccades. The proposed algorithm is partly based on modeling saccadic waveforms as piecewise-quadratic signals. The algorithm first decreases noise in acquired eye-tracking data using optimization to minimize a prescribed objective function, then uses velocity thresholding to detect saccades. Using both simulated saccades and real saccades generated by healthy subjects and patients, we evaluate the performance of the proposed algorithm and 10 other detection algorithms. We show the proposed algorithm is more accurate in detecting both normal and slow saccades than other algorithms.

Original languageEnglish
Pages (from-to)1-18
Number of pages18
JournalJournal of Vision
Volume21
Issue number6
DOIs
StatePublished - Jun 2021
Externally publishedYes

Keywords

  • saccade detection
  • saccade quantitative analysis
  • slow saccades

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