EMPIRICAL MODE DECOMPOSITION-BASED PROCESSING for AUTOMATED DETECTION of EPILEPSY

G. Muralidhar Bairy, Yuki Hagiwara

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Article number1940003
JournalJournal of Mechanics in Medicine and Biology
Volume19
Issue number1
DOIs
Publication statusPublished - 01-02-2019

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

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