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 ´baseline´ 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.