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A comparative study on machine learning approach towards epileptiform eeg signals detection

Electroencephalogram (EEG) signal is extensively used for the diagnosis of various kinds of neurological brain disorders. The classification of normal and abnormal electrical brain spikes through visual inspection is highly subjective and varying across medical experts. Hence, in this project, comp...

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Main Author: Oh, Pearly Bei Qing
Format: Final Year Project Report
Language:English
English
Published: Universiti Malaysia Sarawak, (UNIMAS) 2017
Subjects:
L Education (General)
Q Science (General)
Online Access:http://ir.unimas.my/id/eprint/20936/1/A%20comparative%20study%20on%20machine%20learning%20approach...%2824%20pgs%29.pdf
http://ir.unimas.my/id/eprint/20936/8/PEARLY%20OH%20BEl%20QING.pdf
http://ir.unimas.my/id/eprint/20936/
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spelling my.unimas.ir.209362024-02-20T07:18:07Z http://ir.unimas.my/id/eprint/20936/ A comparative study on machine learning approach towards epileptiform eeg signals detection Oh, Pearly Bei Qing L Education (General) Q Science (General) Electroencephalogram (EEG) signal is extensively used for the diagnosis of various kinds of neurological brain disorders. The classification of normal and abnormal electrical brain spikes through visual inspection is highly subjective and varying across medical experts. Hence, in this project, comparisons between multiple supervised learning approaches are presented, in order to discriminate those epileptiform EEG signals data from non-epileptiform with high generalizability and promising results. Furthermore, both Discrete Wavelet Transform (DWT) and Independent Component Analysis (ICA) are incorporated respectively as a preprocessing stage on reducing dimensionality, besides removing unnecessary noise adequately. Then, a set of statistical extracted features are served as input parameters to various machine learning classifiers, namely Multilayer Perceptron Neural Network (MLPNN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) respectively with two discrete outputs (normal or epilepsy). As a result, the experimental outputs inferred that the wavelet coefficients which extracted by DWT, have demonstrated as the most well representation of EEG signals. Hence, the ANFIS classifier which trained on these salient features using combination of neural network learning capabilities and fuzzy logic decision making approach, has depicted the highest classification performance accuracy of 99.35% (ICA+ANFIS) and 99.67% (DWT+ANFIS) respectively. Meanwhile, others classifiers namely, MLPNN and SVM, have also proven the diagnostic results with potentially high accuracies, which are 94.39% (ICA+MLPNN), 93.67% (DWT+MLPNN), and 96.22% (ICA+SVM), 94.39% (DWT+SVM) respectively, after tuning the system parameters. Therefore, these findings yield promising outcomes to be presented as a framework for training and testing epileptic prediction on EEG ix data by configuring intelligent devices, so that every patient can be treated in an optimum manner, prior to surgical evaluation. Universiti Malaysia Sarawak, (UNIMAS) 2017 Final Year Project Report NonPeerReviewed text en http://ir.unimas.my/id/eprint/20936/1/A%20comparative%20study%20on%20machine%20learning%20approach...%2824%20pgs%29.pdf text en http://ir.unimas.my/id/eprint/20936/8/PEARLY%20OH%20BEl%20QING.pdf Oh, Pearly Bei Qing (2017) A comparative study on machine learning approach towards epileptiform eeg signals detection. [Final Year Project Report] (Unpublished)
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
English
topic L Education (General)
Q Science (General)
spellingShingle L Education (General)
Q Science (General)
Oh, Pearly Bei Qing
A comparative study on machine learning approach towards epileptiform eeg signals detection
description Electroencephalogram (EEG) signal is extensively used for the diagnosis of various kinds of neurological brain disorders. The classification of normal and abnormal electrical brain spikes through visual inspection is highly subjective and varying across medical experts. Hence, in this project, comparisons between multiple supervised learning approaches are presented, in order to discriminate those epileptiform EEG signals data from non-epileptiform with high generalizability and promising results. Furthermore, both Discrete Wavelet Transform (DWT) and Independent Component Analysis (ICA) are incorporated respectively as a preprocessing stage on reducing dimensionality, besides removing unnecessary noise adequately. Then, a set of statistical extracted features are served as input parameters to various machine learning classifiers, namely Multilayer Perceptron Neural Network (MLPNN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) respectively with two discrete outputs (normal or epilepsy). As a result, the experimental outputs inferred that the wavelet coefficients which extracted by DWT, have demonstrated as the most well representation of EEG signals. Hence, the ANFIS classifier which trained on these salient features using combination of neural network learning capabilities and fuzzy logic decision making approach, has depicted the highest classification performance accuracy of 99.35% (ICA+ANFIS) and 99.67% (DWT+ANFIS) respectively. Meanwhile, others classifiers namely, MLPNN and SVM, have also proven the diagnostic results with potentially high accuracies, which are 94.39% (ICA+MLPNN), 93.67% (DWT+MLPNN), and 96.22% (ICA+SVM), 94.39% (DWT+SVM) respectively, after tuning the system parameters. Therefore, these findings yield promising outcomes to be presented as a framework for training and testing epileptic prediction on EEG ix data by configuring intelligent devices, so that every patient can be treated in an optimum manner, prior to surgical evaluation.
format Final Year Project Report
author Oh, Pearly Bei Qing
author_facet Oh, Pearly Bei Qing
author_sort Oh, Pearly Bei Qing
title A comparative study on machine learning approach towards epileptiform eeg signals detection
title_short A comparative study on machine learning approach towards epileptiform eeg signals detection
title_full A comparative study on machine learning approach towards epileptiform eeg signals detection
title_fullStr A comparative study on machine learning approach towards epileptiform eeg signals detection
title_full_unstemmed A comparative study on machine learning approach towards epileptiform eeg signals detection
title_sort comparative study on machine learning approach towards epileptiform eeg signals detection
publisher Universiti Malaysia Sarawak, (UNIMAS)
publishDate 2017
url http://ir.unimas.my/id/eprint/20936/1/A%20comparative%20study%20on%20machine%20learning%20approach...%2824%20pgs%29.pdf
http://ir.unimas.my/id/eprint/20936/8/PEARLY%20OH%20BEl%20QING.pdf
http://ir.unimas.my/id/eprint/20936/
_version_ 1792160611854974976
score 13.211869

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