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Classification of focal and non-focal EEG signals using optimal geometrical features derived from a second-order difference plot of FBSE-EWT rhythms

Publication Type : Research

Publisher : Elsevier

Source : Artificial Intelligence in Medicine (2023): 102542. (SCI, Impact Factor-7.011).

Url : https://www.sciencedirect.com/science/article/pii/S0933365723000568#:~:text=The%20proposed%20approach%20is%20used,and%20non%2Dfocal%20EEG%20signals.&text=The%20significant%20outcome%20of%20the,compared%20to%20other%20reported%20methods.

Campus : Coimbatore

School : School of Computing

Year : 2023

Abstract : Manual detection and localization of the brain’s epileptogenic areas using electroencephalogram (EEG) signals is time-intensive and error-prone. An automated detection system is, thus, highly desirable for support in clinical diagnosis. A set of relevant and significant non-linear features plays a major role in developing a reliable, automated focal detection system.

Cite this Research Publication : Arti Anuragi, Dilip Singh Sisodia, and Ram Bilas Pachori . " Classification of focal and non-focal EEG signals using optimal geometrical features derived from a second-order difference plot of FBSE-EWT rhythms ." Artificial Intelligence in Medicine (2023): 102542. (SCI, Impact Factor-7.011).

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