Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition

Abstract Epilepsy is one of the most common neurological disorders characterized by transient and unexpected electrical disturbance of the brain. The electroencephalogram (EEG) is an invaluable measurement for the purpose of assessing brain activities, containing information relating to the differen...

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Bibliographic Details
Published in:Computer methods and programs in biomedicine 2011-12, Vol.104 (3), p.373-381
Main Authors: Pachori, Ram Bilas, Bajaj, Varun
Format: Article
Language:English
Subjects:
Biological and medical sciences
EEG signal analysis
Electroencephalogram (EEG) signal
Electroencephalography - methods
Empirical mode decomposition
Empirical Research
Epilepsy
Epilepsy - physiopathology
Humans
Internal Medicine
Medical sciences
Other
Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects)
Technology. Biomaterials. Equipments. Material. Instrumentation
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Summary:Abstract Epilepsy is one of the most common neurological disorders characterized by transient and unexpected electrical disturbance of the brain. The electroencephalogram (EEG) is an invaluable measurement for the purpose of assessing brain activities, containing information relating to the different physiological states of the brain. It is a very effective tool for understanding the complex dynamical behavior of the brain. This paper presents the application of empirical mode decomposition (EMD) for analysis of EEG signals. The EMD decomposes a EEG signal into a finite set of bandlimited signals termed intrinsic mode functions (IMFs). The Hilbert transformation of IMFs provides analytic signal representation of IMFs. The area measured from the trace of the analytic IMFs, which have circular form in the complex plane, has been used as a feature in order to discriminate normal EEG signals from the epileptic seizure EEG signals. It has been shown that the area measure of the IMFs has given good discrimination performance. Simulation results illustrate the effectiveness of the proposed method.
ISSN:0169-2607
1872-7565
DOI:10.1016/j.cmpb.2011.03.009