Open AccessFeature Fusion: An Application To Biomedical Signal Classification
Author(s) -
A. Sarmah,
Rahul Lahkar,
Sanjib Kumar Kalita,
B K Dev Choudhury
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f7108.038620
Subject(s) - pattern recognition (psychology) , computer science , discrete wavelet transform , artificial intelligence , linear discriminant analysis , feature (linguistics) , wavelet , canonical correlation , data mining , signal (programming language) , correlation , feature extraction , machine learning , wavelet transform , mathematics , linguistics , philosophy , geometry , programming language
Development of a feasible support system for automating staging of neural disorder based on Electroencephalogram (EEG) is essential to speed-up diagnosis process by improving the burden of the clinician of analyzing large volume data and to accelerate large scale research. In this work Discrete wavelet transform (DWT) has been applied to extract statistically independent features and fused the features for effective classification of various EEG signal. The aim of this paper is to present a comparative study of two feature fusion approaches namely Canonical Correlation Analysis (CCA) and Discriminant Correlation Analysis (DCA). Further, our proposed method can be extended to develop a graphical user interface and promote real time implementation.