Back to Search Start Over

Performance Analysis of Entropy Methods in Detecting Epileptic Seizure from Surface Electroencephalograms.

Authors :
Ali E
Udhayakumar RK
Angelova M
Karmakar C
Source :
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2021 Nov; Vol. 2021, pp. 1082-1085.
Publication Year :
2021

Abstract

Physiological signals like Electrocardiography (ECG) and Electroencephalography (EEG) are complex and nonlinear in nature. To retrieve diagnostic information from these, we need the help of nonlinear methods of analysis. Entropy estimation is a very popular approach in the nonlinear category, where entropy estimates are used as features for signal classification and analysis. In this study, we analyze and compare the performances of four entropy methods; namely Distribution entropy (DistEn), Shannon entropy (ShanEn), Renyi entropy (RenEn) and LempelZiv complexity (LempelZiv) as classification features to detect epileptic seizure (ES) from surface Electroencephalography (sEEG) signal. Experiments were conducted on sEEG data from 23 subjects, obtained from the CHB-MIT database of PhysioNet. ShanEn, RenEn and LempelZiv entropy are found to be potential features for accurate and consistent detection of ES from sEEG, across multiple channels and subjects.

Details

Language :
English
ISSN :
2694-0604
Volume :
2021
Database :
MEDLINE
Journal :
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Publication Type :
Academic Journal
Accession number :
34891475
Full Text :
https://doi.org/10.1109/EMBC46164.2021.9629538