eprintid: 13524
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/35/24
datestamp: 2023-11-10 03:28:05
lastmod: 2023-11-10 03:28:05
status_changed: 2023-11-10 01:51:23
type: article
metadata_visibility: show
creators_name: Amin, H.U.
creators_name: Yusoff, M.Z.
creators_name: Ahmad, R.F.
title: A novel approach based on wavelet analysis and arithmetic coding for automated detection and diagnosis of epileptic seizure in EEG signals using machine learning techniques
ispublished: pub
keywords: Biomedical signal processing; Computer aided instruction; Digital arithmetic; Discrete wavelet transforms; Electrophysiology; Learning systems; Machine learning; Neurodegenerative diseases; Neurophysiology; Signal reconstruction; Wavelet analysis, Arithmetic Coding; Classification methods; Classification performance; Computer aided diagnostics; Electroencephalogram signals; Epileptic seizure detection; Epileptic seizures; Machine learning techniques, Electroencephalography, Article; classifier; computer assisted diagnosis; discrete wavelet transform; electroencephalogram; epilepsy; human; machine learning; mental arithmetic; priority journal; seizure; signal processing; wavelet analysis
note: cited By 68
abstract: Epilepsy, a common neurological disorder, is generally detected by electroencephalogram (EEG) signals. Visual inspection and interpretation of EEGs is a slow, time consuming process that is vulnerable to error and subjective variability. Consequently, several efforts to develop automatic epileptic seizure detection and classification methods have been made. The present study proposes a novel computer aided diagnostic technique (CAD) based on the discrete wavelet transform (DWT) and arithmetic coding to differentiate epileptic seizure signals from normal (seizure-free) signals. The proposed CAD technique comprises three steps. The first step decomposes EEG signals into approximations and detail coefficients using DWT while discarding non-significant coefficients in view of threshold criteria; thus, limiting the number of significant wavelet coefficients. The second step converts significant wavelet coefficients to bit streams using arithmetic coding to compute the compression ratio. In the final step, the compression feature set is standardized, whereupon machine-learning classifiers detect seizure activity from seizure-free signals. We employed the widely used benchmark database from Bonn University to compare and validate the technique with results from prior approaches. The proposed method achieved a perfect classification performance (100 accuracy) for the detection of epileptic seizure activity from EEG data, using both linear and non-liner machine-learning classifiers. This CAD technique can thus be considered robust with an extraordinary detection capability that discriminates epileptic seizure activity from seizure-free and normal EEG activity with simple linear classifiers. The method has the potential for efficient application as an adjunct for the clinical diagnosis of epilepsy. © 2019 Elsevier Ltd
date: 2020
publisher: Elsevier Ltd
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074238680&doi=10.1016%2fj.bspc.2019.101707&partnerID=40&md5=a81e485fd789bc36f18ff193daa2d1d5
id_number: 10.1016/j.bspc.2019.101707
full_text_status: none
publication: Biomedical Signal Processing and Control
volume: 56
refereed: TRUE
issn: 17468094
citation:   Amin, H.U. and Yusoff, M.Z. and Ahmad, R.F.  (2020) A novel approach based on wavelet analysis and arithmetic coding for automated detection and diagnosis of epileptic seizure in EEG signals using machine learning techniques.  Biomedical Signal Processing and Control, 56.   ISSN 17468094