Analysis of focal and non-focal EEG signals using bivariate empirical mode decomposition

The chapter presents a new approach of computer aided diagnosis of focal electroencephalogram (EEG) signals by applying bivariate empirical mode decomposition (BEMD). Firstly, the focal and non-focal EEG signals are decomposed using the BEMD, which results in intrinsic mode functions (IMFs) corresponding to each signal. Secondly, bivariate bandwidths namely, amplitude bandwidth, precession bandwidth, and deformation bandwidth are computed for each obtained IMF. Interquartile range (IQR) values of bivariate bandwidths of IMFs are employed as the features for classification. In order to perform classification least squares support vector machine (LS-SVM) is used. The results of the experiment suggest that the computed bivariate bandwidths are significantly useful to discriminate focal EEG signals. The resultant classification accuracy obtained using proposed methodology, applied on the Bern-Barcelona EEG database, is 84.01%. The obtained results are encouraging and the proposed methodology can be helpful for identification of epileptogenic focus.

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A Discrete-Wavelet-Transform- and Hidden-Markov-Model-Based Approach for Epileptic Focus Localization

Dash

Kolekar

2018

Biomedical Signal and Image Processing in Patient Care

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Epilepsy is the most common neurological disorder with 40-50 million people suffering with it worldwide. Epilepsy is not life threatening but it disables the person to a greater extent due to its uncertainty of occurrences. Epilepsy is detected by repeated occurrences of seizure. Seizure can be generated in brain due to abnormal activity of group of neurons caused by brain tumor, genetic problem, infection, hemorrhage etc. Seizure can be detected by observing the variation in Electroencephalogram (EEG) signal. Focal seizure is defined as seizure localized in one lobe of brain. In this chapter discrete wavelet transform and Hidden Markov Model based focal seizure detection method is proposed for epileptic focus localization. EEG signal was decomposed up to level 5 using dual tree complex wavelet transform and entropy features such as collision entropy, minimum and modified sample entropy were extracted. Hidden Markov model was used for classification purpose. Maximum 80% accuracy was achieved in detecting focal and non-focal EEG signal.

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Analysis of focal and non-focal EEG signals using bivariate empirical mode decomposition

Cited by 3 publications

References 12 publications

Time Frequency Image based features for detection of focal EEG signals

Time Frequency Image based features for detection of focal EEG signals

Automated Classification of Focal and Non-Focal EEG Signals Based on Bivariate Empirical Mode Decomposition

A Discrete-Wavelet-Transform- and Hidden-Markov-Model-Based Approach for Epileptic Focus Localization

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