Interictal epileptic discharge EEG detection based on wavelet and multiresolution analysis
- Resource Type
- Conference
- Authors
- Fathillah, Mohd Syakir; Jaafar, Rosmina; Chellappan, Kalaivani; Remli, Rabani; Zainal, Wan Asyraf Wan
- Source
- 2017 7th IEEE International Conference on System Engineering and Technology (ICSET) System Engineering and Technology (ICSET), 2017 7th IEEE International Conference on. :140-144 Oct, 2017
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Electroencephalography
Wavelet transforms
Feature extraction
Entropy
Support vector machines
Dispersion
Wavelet analysis
time frequency analysis
discrete wavelet transform (DWT)
approximate entropy (ApEn)
support vector machine (SVM)
epilepsy
interictal seizure detection
- Language
- ISSN
- 2470-640X
Epileptologists use interictal epileptic discharge (lED) as a marker for epilepsy. The present conventional method to distinguish normal and I ED by an epileptologist's visual screening is tedious and operator dependent. The focus of this paper is to distinguish normal and IED in clinically recorded electroencephalogram (EEG) using discrete wavelet transform. Wavelet multiresolution analysis has been adopted in this study looking into wavelet energy, wavelet entropy and amplitude dispersion in every sub-band. The extracted features were classified using support vector machine (SVM). EEG data were obtained from both online database and Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM) Neurology database. The ability of the proposed algorithm in detecting the presence of IED is 96.5% of accuracy, 100% of sensitivity and 95.5% of specificity. The algorithm has good potential to be used in clinical practice for IED detection with validation against the present clinical detection method.