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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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    SchizoGoogLeNet: The GoogLeNet-Based Deep Feature Extraction Design for Automatic Detection of Schizophrenia
    (Hindawi Ltd, 2022) Siuly, Siuly; Li, Yan; Wen, Peng; Alcin, Omer Faruk
    Schizophrenia (SZ) is a severe and prolonged disorder of the human brain where people interpret reality in an abnormal way. Traditional methods of SZ detection are based on handcrafted feature extraction methods (manual process), which are tedious and unsophisticated, and also limited in their ability to balance efficiency and accuracy. To solve this issue, this study designed a deep learning-based feature extraction scheme involving the GoogLeNet model called SchizoGoogLeNet that can efficiently and automatically distinguish schizophrenic patients from healthy control (HC) subjects using electroencephalogram (EEG) signals with improved performance. The proposed framework involves multiple stages of EEG data processing. First, this study employs the average filtering method to remove noise and artifacts from the raw EEG signals to improve the signal-to-noise ratio. After that, a GoogLeNet model is designed to discover significant hidden features from denoised signals to identify schizophrenic patients from HC subjects. Finally, the obtained deep feature set is evaluated by the GoogleNet classifier and also some renowned machine learning classifiers to find a sustainable classification method for the obtained deep feature set. Experimental results show that the proposed deep feature extraction model with a support vector machine performs the best, producing a 99.02% correct classification rate for SZ, with an overall accuracy of 98.84%. Furthermore, our proposed model outperforms other existing methods. The proposed design is able to accurately discriminate SZ from HC, and it will be useful for developing a diagnostic tool for SZ detection.