An Automated Ocular Microtremor Feature Extraction using the Gabor Thresholding Technique

Abstract

Ocular Microtremor (OMT) is a very fine continuous eye movement which has potential in monitoring and identifying a number of clinical conditions. There is a need for improved analysis and processing techniques to extract useful, quantifiable parameters from the OMT signal. A number of papers have shown the clinical significance of looking at the 'bursts' and 'baseling' patterns of the OMT signal. Analysis to date relies on visual inspection alone. This paper introduces an automated approach to burst/baseline identification based on a time-varying filter using the Gabor transform.

Simultaneous recordings of ocular microtremor and microsaccades with a piezoelectric sensor and a video-oculography system

“…We extracted the microsaccadic component of the piezoelectric sensor data using a wavelet denoising technique (Al-Kalbani et al, 2007) and we detected the microsaccades from the EyeLink II data using a modified version of Engbert and Kliegl’s algorithm (Engbert & Kliegl, 2003) (Fig. 1C; see Materials and Methods for details).…”

“…We measured OMT using the piezoelectric transduction method introduced by Bengi & Thomas (1968a) and refined by Sheahan et al (1993) and Al-Kalbani et al (2007). A silicone tipped piezoelectric bimorph was brought into contact with the sclera in the interpalpebral region near the temporal limbus.…”

“…Simple bandpass filtering of the signal to isolate OMT can cause ringing artifacts in response to microsaccades, whereas cutting out microsaccades causes short periods of data loss. To avoid these issues, a wavelet denoising technique was used to separate the microsaccadic component from the piezoelectric output (Al-Kalbani et al, 2007) (Fig. 1C).…”