An automated ocular microtremor feature extraction using the Gabor thresholding technique

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.