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    Exploring deep residual network based features for automatic schizophrenia detection from EEG
    (Springer, 2023) Siuly, Siuly; Guo, Yanhui; Alcin, Omer Faruk; Li, Yan; Wen, Peng; Wang, Hua
    Schizophrenia is a severe mental illness which can cause lifelong disability. Most recent studies on the Electroencephalogram (EEG)-based diagnosis of schizophrenia rely on bespoke/hand-crafted feature extraction techniques. Traditional manual feature extraction methods are time-consuming, imprecise, and have a limited ability to balance accuracy and efficiency. Addressing this issue, this study introduces a deep residual network (deep ResNet) based feature extraction design that can automatically extract representative features from EEG signal data for identifying schizophrenia. This proposed method consists of three stages: signal pre-processing by average filtering method, extraction of hidden patterns of EEG signals by deep ResNet, and classification of schizophrenia by softmax layer. To assess the performance of the obtained deep features, ResNet softmax classifier and also several machine learning (ML) techniques are applied on the same feature set. The experimental results for a Kaggle schizophrenia EEG dataset show that the deep features with support vector machine classifier could achieve the highest performances (99.23% accuracy) compared to the ResNet classifier. Furthermore, the proposed model performs better than the existing approaches. The findings suggest that our proposed strategy has capability to discover important biomarkers for automatic diagnosis of schizophrenia from EEG, which will aid in the development of a computer assisted diagnostic system by specialists.
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    Exploring Rhythms and Channels-Based EEG Biomarkers for Early Detection of Alzheimer's Disease
    (Ieee-Inst Electrical Electronics Engineers Inc, 2024) Siuly, Siuly; Alcin, Omer Faruk; Wang, Hua; Li, Yan; Wen, Peng
    There is no treatment that permanently cures Alzheimer's disease (AD); however, early detection can alleviate the severe effects of the disease. To support early detection of the different stages of AD (e.g., mild, moderate), the key aim of this study is to develop a computer aided diagnostic (CAD) framework that include a long short-term memory (LSTM) network using massive multi-channel electroencephalogram (EEG) data. Although EEG rhythms and EEG channels jointly possess important biomarkers that may be used for diagnosis of AD, but the traditional methods did not explore this issue in any research. To address this problem, this study introduces a new framework to identify the optimal EEG rhythms and channels required for the diagnosis of AD. The proposed framework was tested on a real-time AD EEG dataset. The results reveal that together, the gamma and beta rhythms in the channels, Cz, F4, P4, T6, Pz were the most reliable biomarker for identifying AD and the proposed LSTM based model yielded the best performance. Additionally, another mild cognitive impairment (MCI) EEG dataset was used to test the proposed approach, and the results were excellent (accuracy>99%). The proposed framework will be useful for creating a CAD system to perform automatic AD diagnosis.
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    A New Framework for Automatic Detection of Patients With Mild Cognitive Impairment Using Resting-State EEG Signals
    (IEEE (Institute of Electrical and Electronics Engineers), 2020) Siuly, Siuly; Alçin, Ömer Faruk; Kabir, Enamul; Şengür, Abdulkadir; Wang, Hua; Zhang, Yanchun; Whittaker, Frank
    Mild cognitive impairment (MCI) can be an indicator representing the early stage of Alzheimier's disease (AD). AD, which is the most common form of dementia, is a major public health problem worldwide. Efficient detection of MCI is essential to identify the risks of AD and dementia. Currently Electroencephalography (EEG) is the most popular tool to investigate the presenence of MCI biomarkers. This study aims to develop a new framework that can use EEG data to automatically distinguish MCI patients from healthy control subjects. The proposed framework consists of noise removal (baseline drift and power line interference noises), segmentation, data compression, feature extraction, classification, and performance evaluation. This study introduces Piecewise Aggregate Approximation (PAA) for compressing massive volumes of EEG data for reliable analysis. Permutation entropy (PE) and auto-regressive (AR) model features are investigated to explore whether the changes in EEG signals can effectively distinguish MCI from healthy control subjects. Finally, three models are developed based on three modern machine learning techniques: Extreme Learning Machine (ELM); Support Vector Machine (SVM) and K-Nearest Neighbours (KNN) for the obtained feature sets. Our developed models are tested on a publicly available MCI EEG database and the robustness of our models is evaluated by using a 10-fold cross validation method. The results show that the proposed ELM based method achieves the highest classification accuracy (98.78%) with lower execution time (0.281 seconds) and also outperforms the existing methods. The experimental results suggest that our proposed framework could provide a robust biomarker for efficient detection of MCI patients.