Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals
By Rajeev Sharma, Ram Bilas Pachori, Usha R. Acharya. Entropy, 2015
299 citations recorded by the source corpus.
The brain is a complex structure made up of interconnected neurons, and its electrical activities can be evaluated using electroencephalogram (EEG) signals. The characteristics of the brain area affected by partial epilepsy can be studied using focal and non-focal EEG signals. In this work, a method for the classification of focal and non-focal EEG signals is presented using entropy measures. These entropy measures can be useful in assessing the nonlinear interrelation and complexity of focal and non-focal EEG signals. These EEG signals are first decomposed using the empirical mode decomposition (EMD) method to extract intrinsic mode functions (IMFs). The entropy features, namely, average Shannon entropy (ShEnAvg), average Renyi’s entropy (RenEnAvg ), average approximate entropy (ApEnAvg), average sample entropy (SpEnAvg) and average phase entropies (S1Avg and S2Avg), are computed from different IMFs of focal and non-focal EEG signals. These entropies are used as the input feature set for the least squares support vector machine (LS-SVM) classifier to classify into focal and non-focal EEG signals. Experimental results show that our proposed method is able to differentiate the focal and non-focal EEG signals with an average classification accuracy of 87% correct.
Subfields: Cognitive Neuroscience, Control and Systems Engineering, Signal Processing
View Paper: https://www.semanticscholar.org/paper/8401b8001489514741809c2d9a17b21a57944103
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In Cognitive Neuroscience
Citations in the corpus
- Stock closing price prediction based on sentiment analysis and LSTM (2019, unknown): “Therefore, it has been applied quickly and effectively in different engineering fields, such as focal electroencephalogram signals [37], wind speed [38] and automatic bearing fault diagnosis [39].”
- Dispersion Entropy: A Measure for Time-Series Analysis (2016, background): “focal and nonfocal signals as this kind of biomedical data is a popular candidate to evaluate entropy metrics [16]–[18].”