Non-linear analysis of intracranial electroencephalogram recordings with approximate entropy and Lempel-Ziv complexity for epileptic seizure detection.
Epileptic seizures are generated by an abnormal synchronization of neurons unforeseeable for the patients. In this study we analyzed invasive electroencephalogram (EEG) recordings in patients suffering from medically intractable focal epilepsy with two non-linear methods, Approximate Entropy (ApEn) and Lempel-Ziv (LZ) complexity. ApEn and LZ complexity quantify the regularity and complexity of a time series, respectively, and are well suited to the analysis of non-stationary biomedical signals of short length. Our results show an increase in ApEn and LZ complexity values during seizures at the focal electrodes. These changes could also be seen at some extra focal electrodes. After the seizure ends, the values of both non-linear metrics return to values lower than those before the seizure. Moreover, we quantified the changes in LZ complexity, showing the complexity increase during the seizure and its notable decrease after its end. Our results suggest that these techniques are useful to detect changes due to epileptic seizures in the EEG.
Abásolo
Daniel
D
Biomedical Engineering Group, Department of Signal Theory and Communications, ETSI de Telecomunicación, University of Valladolid, Camino del Cementerio s/n, 47011, Valladolid, Spain. daniel.abasolo@tel.uva.es
James
Christopher J
CJ
Hornero
Roberto
R
D000328:Adult; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted; D004569:Electroencephalography / Q000295:instrumentation* / Q000379:methods*; D004582:Electronics, Medical; D019277:Entropy; D004827:Epilepsy / Q000175:diagnosis* / Q000473:pathology*; D004867:Equipment Design; D005260:Female; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated; D010865:Pilot Projects; D012984:Software