Objectives: The aim of this research has been to introduce an automatic method, simple from the mathematical and computational points of view, for the recognition and classification of the A-phases of the cyclic alternating pattern. Methods: The automatic method was based on the computation of 5 descriptors, which were derived from the EEG signal and were able to provide a meaningful data reduction. Each of them corresponded to a different frequency band. Results: The computation of these descriptors, followed by the introduction of two suitable thresholds and of simple criteria for logical discrimination, provided results which were in good agreement with those obtained with visual analysis. The method was versatile and could be applied to the study of other important microstructure phenomena by means of very small adaptations. Conclusions: The simplicity of the method leads to a better understanding and a more precise definition of the visual criteria for the recognition and classification of the microstructure phenomena.

An automatic method for the recognition and classification of the A-phases of the cyclic alternating pattern

BARCARO, UMBERTO;BONANNI, ENRICA;MURRI, LUIGI
2002

Abstract

Objectives: The aim of this research has been to introduce an automatic method, simple from the mathematical and computational points of view, for the recognition and classification of the A-phases of the cyclic alternating pattern. Methods: The automatic method was based on the computation of 5 descriptors, which were derived from the EEG signal and were able to provide a meaningful data reduction. Each of them corresponded to a different frequency band. Results: The computation of these descriptors, followed by the introduction of two suitable thresholds and of simple criteria for logical discrimination, provided results which were in good agreement with those obtained with visual analysis. The method was versatile and could be applied to the study of other important microstructure phenomena by means of very small adaptations. Conclusions: The simplicity of the method leads to a better understanding and a more precise definition of the visual criteria for the recognition and classification of the microstructure phenomena.
Navona, C; Barcaro, Umberto; Bonanni, Enrica; DI MARTINO, F; Maestri, M; Murri, Luigi
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11568/188222
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