Epileptic seizure detection using wavelet transform based sample entropy and support vector machine

Electroencephalogram is the recording of brain electrical activity and it contains valuable information related to the different physiological states of the brain. In this study, we present a new approach to detect epileptic seizure. The new scheme was based on discrete wavelet transform and sample entropy analysis of EEG signals. Decision making is performed in two stages: feature extraction by computing the wavelet coefficients and the sample entropy and detection by using support vector machine. The analysis results depicted that during seizure activity EEG had lower sample entropy values compared to normal EEG. This suggested that epileptic EEG was more predictable or less complex than the normal EEG.

[1]  Hasan Ocak,et al.  Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy , 2009, Expert Syst. Appl..

[2]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[3]  U. Rajendra Acharya,et al.  Entropies for detection of epilepsy in EEG , 2005, Comput. Methods Programs Biomed..

[4]  Abdulhamit Subasi.  Automatic detection of epileptic seizure using dynamic fuzzy neural networks , 2006, Expert Syst. Appl..

[5]  B. Litt,et al.  For Personal Use. Only Reproduce with Permission from the Lancet Publishing Group. Review Prediction of Epileptic Seizures Are Seizures Predictable? Prediction of Epileptic Seizures , 2022 .

[6]  Abdulhamit Subasi,et al.  EEG signal classification using wavelet feature extraction and a mixture of expert model , 2007, Expert Syst. Appl..

[7]  P. Grassberger,et al.  NONLINEAR TIME SEQUENCE ANALYSIS , 1991 .

[8]  Brian Litt,et al.  Human and automated detection of high-frequency oscillations in clinical intracranial EEG recordings , 2007, Clinical Neurophysiology.

[9]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[10]  Pietro Liò,et al.  A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine , 2010 .

[11]  N Pradhan,et al.  Detection of seizure activity in EEG by an artificial neural network: a preliminary study. , 1996, Computers and biomedical research, an international journal.

[12]  J. Gotman,et al.  Automatic seizure detection in the newborn: methods and initial evaluation. , 1997, Electroencephalography and clinical neurophysiology.

[13]  Elif Derya Übeyli,et al.  Application of adaptive neuro-fuzzy inference system for detection of electrocardiographic changes in patients with partial epilepsy using feature extraction , 2004, Expert Syst. Appl..