Feature Fusion: An Application To Biomedical Signal Classification | Zendy

Arup Sarmah | Zendy; Rahul Lahkar | Zendy; Sanjib Kumar Kalita | Zendy; B K Dev Choudhury | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Feature Fusion: An Application To Biomedical Signal Classification

Author(s) -

Arup Sarmah,

Rahul Lahkar,

Sanjib Kumar Kalita,

B K Dev Choudhury

Publication year - 2020

Publication title -

international journal of recent technology and engineering (ijrte)

Language(s) - English

Resource type - Journals

ISSN - 2277-3878

DOI - 10.35940/ijrte.f7108.038620

Subject(s) - computer science , pattern recognition (psychology) , discrete wavelet transform , artificial intelligence , linear discriminant analysis , feature (linguistics) , canonical correlation , wavelet , signal (programming language) , correlation , data mining , 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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research