Epileptic Seizure Detection Using Artifact Reduction and HOS Features of WPD

Epileptic Seizure Detection Using Artifact Reduction and HOS Features of WPD

The use of computer aided diagnosis systems for disease identifiscation, based on signal processing, image processing and video processing terminologies is common due to emerging technologies in medical field. The detection of epilepsy seizures using EEG recordings is done by different signal proces...

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Bibliographic Details
Published in:ITM Web of Conferences Vol. 32; p. 2008
Main Authors: Vijay Kakade, Meenal, Gaikwad, Chandrakant J., Dahake, Vijay R.
Format: Journal Article Conference Proceeding
Language:English
Published: Les Ulis EDP Sciences 2020
Subjects:
Algorithms
Convulsions & seizures
eeg
Electroencephalography
Epilepsy
Feature extraction
higher order statistics
Image processing
New technology
seizure
Seizures
Signal processing
stationary wavelet transform
Video
wavelet packet decomposition
Wavelet transforms
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Summary:The use of computer aided diagnosis systems for disease identifiscation, based on signal processing, image processing and video processing terminologies is common due to emerging technologies in medical field. The detection of epilepsy seizures using EEG recordings is done by different signal processing techniques. To reduce the disability caused by the uncertainty of the occurrence of seizures, a recording system which shall result accurate and early detection of seizure with quick warning is greatly desired. To optimize the performance of EEG based epilepsy seizures detection, in this work we are presenting a method based on two key algorithms. Here, we propose unique algorithm based on SWT (Stationary Wavelet Transform), for easier seizure analysis process, along with improved performance of the application of seizure detection algorithms. Then, we propose the algorithm for feature extraction that makes use of Higher Order Statistics of the coefficients that are calculated using Wavelet Packet Decomposition (WPD).This helps in improving the epilepsy seizures detection performance. The proposed methods helps to improve the overall efficiency and robustness of EEG based epilepsy seizures detection system.
ISSN:2271-2097
2431-7578
2271-2097
DOI:10.1051/itmconf/20203202008