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A blind source separation algorithm for the processing and classification of electro-oculogram data

Abstract:

Abnormalities in the oculomotor system are well known clinical symptoms in patients of several neurodegenerative diseases, including modifications in latency, peak velocity, and deviation in saccadic movements, causing changes in the waveform of the patient response. The changes in the morphology waveform suggest a higher degree of statistic independence in sick patients when compared to healthy individuals regarding the patient response to the visual saccadic stimulus modeled by means of digital generated saccade waveforms. The electro-oculogram records of six patients diagnosed with ataxia SCA2 (a neurodegenerative hebkp_reditary disease) and six healthy subjects used as control were processed to extract saccades. We propose the application of a blind source separation algorithm (or independent component analysis algorithm) in order to find significant differences in the obtained estimations between healthy and sick subjects. These results point out the validity of independent component analysis based techniques as an adequate tool in order to evaluate saccadic waveform changes in patients of ataxia SCA-2.