Among various entropy features, EMD-Rényi combination (79% accuracy) provides higher classification accuracy. Approximate entropy extracted from IMFs
produced an accuracy of 75%. Shannon entropy with EMD is not a good choice of
feature for epileptic seizure detection. The classification accuracy produced by
Shannon entropy is only 58%. Complexity of EEG data is the reason for less percentage of accuracy.
Three fractal dimensions (Petrosian fractal dimension, Higuchi fractal dimension, Katz fractal dimension) used in this work also produce promising results when
they are extracted from IMFs. In this study, EMD based Katz fractal dimension
produces higher (80%) classification accuracy than Petrosian (75%) fractal dimension and Higuchi (71%) fractal dimension.
EMD based statistical features did not produce promising results for classification of epileptic EEG signals. But the results are comparatively better than the
features from time domain and DWT domain. The highest classification accuracy
(84%) reported in this study is with newly introduced exponential energy feature
by Fasil and Rajesh [26]. Exponential energy feature utilizes the detailed information available in IMFs to classify epileptic EEG signals effectively. The achieved
results show the effectiveness of empirical mode decomposition (EMD) as major
step in epilepsy classification.
7. Conclusions
The scope of the empirical mode decomposition of EEG signals in effectual
classification of seizure is studied in this work. Four intrinsic mode functions
(IMFs) are obtained by applying EMD on filtered EEG signals. Widely used features
such as entropy features, fractal dimension features, statistical features and exponential energy features are extracted and its discriminating power is studied. SVM
with RBF kernel is used for the classification task. Exponential energy feature
provided better results for the seizure classification.
Seizure identification is a challenging and risk bearing activity, which require
better accuracy. In future, authors will concentrate on improving the results by
incorporating other signal transformation methods with EMD.
Conflict of interest
Authors declare no conflict of interest.
73
Empirical Mode Decomposition of EEG Signals for the Effectual Classification of Seizures
DOI: http://dx.doi.org/10.5772/intechopen.89017
produced an accuracy of 75%. Shannon entropy with EMD is not a good choice of
feature for epileptic seizure detection. The classification accuracy produced by
Shannon entropy is only 58%. Complexity of EEG data is the reason for less percentage of accuracy.
Three fractal dimensions (Petrosian fractal dimension, Higuchi fractal dimension, Katz fractal dimension) used in this work also produce promising results when
they are extracted from IMFs. In this study, EMD based Katz fractal dimension
produces higher (80%) classification accuracy than Petrosian (75%) fractal dimension and Higuchi (71%) fractal dimension.
EMD based statistical features did not produce promising results for classification of epileptic EEG signals. But the results are comparatively better than the
features from time domain and DWT domain. The highest classification accuracy
(84%) reported in this study is with newly introduced exponential energy feature
by Fasil and Rajesh [26]. Exponential energy feature utilizes the detailed information available in IMFs to classify epileptic EEG signals effectively. The achieved
results show the effectiveness of empirical mode decomposition (EMD) as major
step in epilepsy classification.
7. Conclusions
The scope of the empirical mode decomposition of EEG signals in effectual
classification of seizure is studied in this work. Four intrinsic mode functions
(IMFs) are obtained by applying EMD on filtered EEG signals. Widely used features
such as entropy features, fractal dimension features, statistical features and exponential energy features are extracted and its discriminating power is studied. SVM
with RBF kernel is used for the classification task. Exponential energy feature
provided better results for the seizure classification.
Seizure identification is a challenging and risk bearing activity, which require
better accuracy. In future, authors will concentrate on improving the results by
incorporating other signal transformation methods with EMD.
Conflict of interest
Authors declare no conflict of interest.
73
Empirical Mode Decomposition of EEG Signals for the Effectual Classification of Seizures
DOI: http://dx.doi.org/10.5772/intechopen.89017