Feature Fusion: An Application To Biomedical Signal Classification
Arup Sarmah1, Rahul Lahkar2, Sanjib Kalita3, B K Dev Choudhury4
1Arup Sarmah*, Department of Computer Science, Pub Kamrup College, Assam, India.
2Rahul Lahkar, Department of Computer Science, Pub Kamrup College, Assam, India.
3Sanjib Kalita, department of Computer Science, Gauhati University, Kamrup, India.
4B K Dev Choudhury, Pub Kamrup College, Assam, India.
Manuscript received on February 28, 2020. | Revised Manuscript received on March 22, 2020. | Manuscript published on March 30, 2020. | PP: 5844-5849 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7108038620/2020©BEIESP | DOI: 10.35940/ijrte.F7108.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: EEG, Feature fusion, CCA, DCA.
Scope of the Article: Classification.