Empirical mode decomposition-based Processing for automated detection Of epilepsy

Bairy, Muralidhar G and Hagiwara, Yuki (2019) Empirical mode decomposition-based Processing for automated detection Of epilepsy. Journal of Mechanics in Medicine and Biology, 19 (1). pp. 1940003-1. ISSN 1793-6810

[img] PDF
6238.pdf - Published Version
Restricted to Registered users only

Download (946kB) | Request a copy


Epilepsy is a chronic illness of the brain characterized by recurring seizure attacks. Electroencephalogram (EEG) can record the electrical activity of the brain and is extensively used to analyze and diagnose epileptic seizures. However, the EEG signals are highly non-linear and chaotic and are difficult to analyze due to their small magnitude. Hence, empirical mode decomposition (EMD), a non-linear technique, has been widely adopted to capture the subtle changes present in the EEG signals. Hence, it is an added advantage to develop an automated computer-aided diagnostic (CAD) system to detect the different brain activities from the EEG signals using machine learning approaches. In this paper, we focus on the previous works which have used the EMD technique in the automated detection of normal or epileptic EEG signals

Item Type: Article
Uncontrolled Keywords: Empirical mode decomposition; encephalogram; epilepsy; seizure; time-frequency method.
Subjects: Engineering > MIT Manipal > Biomedical
Depositing User: MIT Library
Date Deposited: 09 Mar 2019 04:21
Last Modified: 09 Mar 2019 04:21
URI: http://eprints.manipal.edu/id/eprint/153406

Actions (login required)

View Item View Item