CLASSIFICATION OF EEG SIGNALS IN NORMAL AND DEPRESSION CONDITIONS BY ANN USING RWE AND SIGNAL ENTROPY

EEG is useful for the analysis of the functional activity of the brain and a detailed assessment of this non-stationary waveform can provide crucial parameters indicative of the mental state of patients. The complex nature of EEG signals calls for automated analysis using various signal processing m...

Full description

Saved in:
Bibliographic Details
Published inJournal of mechanics in medicine and biology Vol. 12; no. 4; pp. 1240019 - 1240013
Main Authors PUTHANKATTIL, SUBHA D., JOSEPH, PAUL K.
Format Journal Article
LanguageEnglish
Published World Scientific Publishing Company 01.09.2012
Subjects
Classification
Depression
Entropy
Feedforward
Learning theory
Neural networks
Patients
Signal processing
wavelet entropy (WE)
EEG
discrete wavelet transform (DWT)
relative wavelet energy (RWE)
artificial feedforward neural network
Parseval's theorem
multiresolution decomposition
Online AccessGet full text

Cover

More Information
Summary:EEG is useful for the analysis of the functional activity of the brain and a detailed assessment of this non-stationary waveform can provide crucial parameters indicative of the mental state of patients. The complex nature of EEG signals calls for automated analysis using various signal processing methods. This paper attempts to classify the EEG signals of normal and depression patients using well-established signal processing techniques involving relative wavelet energy (RWE) and artificial feedForward neural network. High frequency noise present in the recorded signal is removed using total variation filtering (TVF). Classification of the frequency bands of EEG signals into appropriate detail levels and approximation level is carried out using an eight-level multiresolution decomposition method of discrete wavelet transform (DWT). Parseval's theorem is used for calculating the energy at different resolution levels. RWE analysis gives information about the signal energy distribution at different decomposition levels. Both RWE and feedforward Network are used to classify the signals from normal controls and depression patients. The performance of the artificial neural network was evaluated using the classification accuracy and its value of 98.11% indicates a great potential for classifying normal and depression signals.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0219-5194
1793-6810
DOI:10.1142/S0219519412400192