NDLI: Accurate classification of epilepsy seizure types using wavelet packet decomposition and local detrended fluctuation analysis

Content Provider	IET Digital Library
Author	Tang, Lihan Zhao, Menglian Wu, Xiaobo
Abstract	Electroencephalogram (EEG) signals are widely used in diagnosis of epilepsy. Accurate classification of seizure types based on EEG signals can provide vital information for diagnosis and treatment. Since visual inspection and interpretation of seizure types are time consuming and prone to errors, a novel classification method combining wavelet packet decomposition (WPD) and local detrended fluctuation analysis (L-DFA) is proposed for the computer-aided diagnostic system. The proposed method is able to classify a wide variety of seizures automatically and accurately. As the first step towards this goal, raw EEG signals are decomposed by WPD according to intrinsic frequency bands of human brain. Then L-DFA is applied to characterise the dynamical fractal structure of sub-band signals. Finally, EEG signals are classified by support vector machine based on the combined fractal spectrum features. The experimental results on Temple University Hospital database show that the proposed method achieves a total classification accuracy of 97.80%, outperforming existing methods based on the same database.
Starting Page	861
Ending Page	863
Page Count	3
ISSN	00135194
Volume Number	56
e-ISSN	1350911X
Issue Number	Issue 17, Aug (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/56/17
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2020.1471
Journal	Electronics Letters
Publisher Date	2020-06-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bioelectric Signal Biology And Medical Computing Combined Fractal Spectrum Features Computer Vision And Image Processing Technique Computer-aided Diagnostic System Digital Signal Processing Electrical Activity in Neurophysiological Processes Electrodiagnostics And Other Electrical Measurement Technique Electroencephalogram Signal Electroencephalography Epilepsy Seizure Types Fractals Intrinsic Frequency Band Knowledge Engineering Technique L-DFA Local Detrended Fluctuation Analysis Medical Signal Processing Novel Classification Method Probability Theory Raw EEG Signal Signal Classification Signal Processing And Detection Statistics Stochastic Linearised SCUC Sub-band Signal Support Vector Machine Total Classification Accuracy Visual Inspection Vital Information Wavelet Packet Decomposition Wavelet Transform WPD
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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