Processing Polysomnographic Signals, using Independent Component Analysis Approaches

R. Sameni, M.B. Shamsollahi (Iran), and L. Senhadji (France)


Polysomnographic signals, Independent Component Analysis, Biomedical signal artifacts.


In this paper several applications of the Independent Component Analysis (ICA) algorithm, for the analysis of biomedical signal recordings have been investigated. One of these applications is the removal of EEG artifacts such as the EOG. It is shown that ICA may serve as a powerful tool, which could help the analysis of biomedical recordings, and give better insights about the underlying sources of some disorders. Another application of the proposed method is the detection of sleep disorders in patients suffering from sleep apnea. The ultimate goal of this approach is to develop an automatic noninvasive data acquisition system, for clinical applications.

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