Real-time epilepsy seizure detection and brain connectivity analysis using electroencephalogram

PhD by Publication


Shen, Mingkan. 2023. Real-time epilepsy seizure detection and brain connectivity analysis using electroencephalogram. PhD by Publication Doctor of Philosophy. University of Southern Queensland. https://doi.org/10.26192/z1v0w
Title

Real-time epilepsy seizure detection and brain connectivity analysis using electroencephalogram

TypePhD by Publication
AuthorsShen, Mingkan
Supervisor
1. FirstProf Paul Wen
2. SecondProf Yan Li
3. ThirdDr Bo Song
Institution of OriginUniversity of Southern Queensland
Qualification NameDoctor of Philosophy
Number of Pages129
Year2023
PublisherUniversity of Southern Queensland
Place of PublicationAustralia
Digital Object Identifier (DOI)https://doi.org/10.26192/z1v0w
Abstract

This thesis integrates signal processing techniques, functional brain connectivity analysis, and artificial intelligence methods to address the challenges of real-time epilepsy seizure detection and provide insights into complex brain disorders, such as alcoholism and schizophrenia (ScZ). By leveraging electroencephalogram (EEG) data and advanced analysis methods, the research aims to achieve high accuracy detection and identification of biomarkers for these conditions. The real-time epilepsy seizure detection involves three main steps: pre-processing, feature extraction, and machine learning (ML) or deep learning (DL) models for classification and detection. Various signal processing methods, such as discrete wavelet transform, tunable-Q wavelet transform, and short-time Fourier transform, are applied in the pre-processing stage to decompose the EEG signals into time domain, frequency domain, and time-frequency domain data. Feature extraction techniques, such as statistical moments, entropy algorithms, and power spectrum analysis, are used to detect the sharp waves indicative of seizure activity. ML and DL models, such as support vector machines and convolutional neural networks (CNN), are employed to address the robustness challenges and provide high accuracy classification results for seizure detection. The EEG brain connectivity analysis focus on the correlations between different EEG channel signals within the complex brain network. Alcoholism and ScZ datasets are utilized in three experiments. The signal processing methods include continuous wavelet transform and multivariate autoregressive models to extract relevant features from the EEG signals. Functional brain connectivity is then calculated using mutual information and coherence algorithms. DL models, particularly CNN, are employed to classify patients and healthy control subjects based on the calculated functional connectivity. Furthermore, statistical analysis of the entire brain connectivity is conducted to identify biomarkers of abnormal connectivity patterns associated with complex brain disorders.

KeywordsEEG; machine learning; epilepsy seizure detection; functional brain connectivity; signal processing; deep learning; complex brain network
Related Output
Has partAn EEG based real-time epilepsy seizure detection approach using discrete wavelet transform and machine learning methods
Has partReal-time epilepsy seizure detection based on EEG using tunable-Q wavelet transform and convolutional neural network
Has partDetection of alcoholic EEG signals based on whole brain connectivity and convolution neural networks
Has partAutomatic identification of schizophrenia based on EEG signals using dynamic functional connectivity analysis and 3D convolutional neural network
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020400399. Biomedical engineering not elsewhere classified
Public Notes

File reproduced in accordance with the copyright policy of the publisher/author.

Byline AffiliationsSchool of Engineering
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Related outputs

3D convolutional neural network for schizophrenia detection using as EEG-based functional brain network
Shen, Mingkan, Wen, Peng, Song, Bo and Li, Yan. 2024. "3D convolutional neural network for schizophrenia detection using as EEG-based functional brain network." Biomedical Signal Processing and Control. 89. https://doi.org/10.1016/j.bspc.2023.105815
Automatic identification of schizophrenia based on EEG signals using dynamic functional connectivity analysis and 3D convolutional neural network
Shen, Mingkan, Wen, Peng, Song, Bo and Li, Yan. 2023. "Automatic identification of schizophrenia based on EEG signals using dynamic functional connectivity analysis and 3D convolutional neural network." Computers in Biology and Medicine. 160. https://doi.org/10.1016/j.compbiomed.2023.107022