Browsing by Author "Xu, Guizhi"

Now showing 1 - 8 of 8
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    Application of Deep Learning and WT-SST in Localization of Epileptogenic Zone Using Epileptic EEG Signals
    (MDPI, 2022-05-11) Saminu, Sani; Xu, Guizhi; Zhang, Shuai; Abd El Kader, Isselmou; Jabire, Adamu Halilu; Ahmed, Yusuf Kola; Karaye, Ibrahim Abdullahi; Ahmad, Isah Salim
    Focal and non-focal Electroencephalogram(EEG) signals have proved to be effective techniques for identifying areas in the brain that are affected by epileptic seizures, known as the epileptogenic zones. The detection of the location of focal EEG signals and the time of seizure occurrence are vital information that help doctors treat focal epileptic seizures using a surgical method. This paper proposed a computer-aided detection (CAD) system for detecting and classifying focal and non-focal EEG signals as the manual process is time-consuming, prone to error, and tedious. The proposed technique employs time-frequency features, statistical, and nonlinear approaches to form a robust features extraction technique. Four detection and classification techniques for focal and non-focal EEG signals were proposed. (1). Combined hybrid features with Support Vector Machine (Hybrid-SVM) (2). Discrete Wavelet Transform with Deep Learning Network (DWT-DNN) (3). Combined hybrid features with DNN (Hybrid-DNN) as an optimized DNN model. Lastly, (4). A newly proposed technique using Wavelet Synchrosqueezing Transform-Deep Convolutional Neural Network (WTSST-DCNN). Prior to feeding the features to classifiers, statistical analyses, including t-tests, were deployed to obtain relevant and significant features at each approach. The proposed feature extraction technique and classification proved effective and suitable for smart Internet of Medical Things (IoMT) devices as performance parameters of accuracy, sensitivity, and specificity are higher than recently related works with a value of 99.7%, 99.5%, and 99.7% respectively.
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    Applications of Artificial Intelligence in Automatic Detection of Epileptic Seizures Using EEG Signals: A Review
    (BON VIEW PUBLISHING PTE. LTD, 2022-07) Saminu, Sani; Xu, Guizhi; Zhang, Shuai; Abd El Kader, Isselmou; Aliyu, Hajara Abdulkarim; Jabire, Adamu Halilu; Ahmed, Yusuf Kola; Adamu, Mohammed Jajere
    Correctly interpreting an electroencephalogram signal with high accuracy is a tedious and time-consuming task that may take several years of manual training due to its complexity, noisy, non-stationarity, and nonlinear nature. To deal with the vast amount of data and recent challenges of meeting the requirements to develop low cost, high speed, low complexity smart internet of medical things computer-aided devices (CAD), artificial intelligence (AI) techniques which consist of machine learning and deep learning (DL) play a vital role in achieving the stated goals. Over the years, machine learning techniques have been developed to detect and classify epileptic seizures. But until recently, DL techniques have been applied in various applications such as image processing and computer visions. However, several research studies have turned their attention to exploring the efficacy of DL to overcome some challenges associated with conventional automatic seizure detection techniques. This article endeavors to review and investigate the fundamentals, applications, and progress of AI-based techniques applied in CAD system for epileptic seizure detection and characterization. It would help in actualizing and realizing smart wireless wearable medical devices so that patients can monitor seizures before their occurrence and help doctors diagnose and treat them. The work reveals that the recent application of DL algorithms improves the realization and implementation of mobile health in a clinical environment.
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    Brain Tumor Identification by Hybrid CNN-SWT Model
    (Bentham Science Publishers Ltd, 2022-05-24) Abd El Kader, Isselmou; Xu, Guizhi; Zhang, Shuai; Saminu, Sani; Javaid, Imran; Ahmad, Isah Salim; Kamhi, Souha
    Objective: Detecting brain tumor using the segmentationtechnique is a big challenge for researchers and takes a long time inmedical image processing. Magnetic resonance image analysis techniquesfacilitate the accurate detection of tissues and abnormal tumors in thebrain. The size of a brain tumor can vary with the individual and thespecifics of the tumor. Radiologists face great difficulty in diagnosing andclassifying brain tumors. Method: This paper proposed a hybrid model-based convolutional neuralnetwork with a stationary wavelet trans-form named “CNN-SWT” tosegment brain tumors using MR brain big data. We utilized 7 layers forclassification in the proposed model that include 3 convolutional and 3ReLU. Firstly, the input MR image is divided into multiple patches, and thenthe central pixel value of each patch is provided to the CNN-SWT. Secondly,the pre-processing stage is per-formed using the mean filter to remove thenoise. Then the convolution neural network-layer approach is utilized tosegment brain tumors. After segmentation, robust feature extraction suchas information-extraction methods is used for the feature extractionprocess. Finally, a CNN-based hybrid scheme based on the stationarywavelet transform technique is used to detect tumors using MR brainimages. Materials: These experiments were obtained using 11500 MR brain imagesdata from the hospital national of oncology. Results: It was proved that the proposed hybrid achieved a highclassification accuracy of (98.7 %) as compared with existing methods. Conclusion: The advantage of the hybrid novelty of the model and theability to detect the tumor area achieved excellent overall performanceusing different values.
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    An Efficient Convolutional Neural Network Model for Brain MRI Segmentation
    (WSEAS, 2022-05-05) Abd El Kader, Isselmou; Xu, Guizhi; Zhang, Shuai; BRAHIM, 2EL MAALOUMA SIDI; Saminu, Sani
    Medical image analysis is a very interesting research area, and it is a significant challenge for researchers. Due to the complexity of the brain structure, accurate diagnosis of brain tumors is extremely difficult. In recent years, research focused on medical image processing to solve this problem by relying on deep learning techniques, and it has achieved good results in this field. This paper proposes an efficient convolutional neural network model for MR brain image segmentation and analysis. The novel model consists of segmentation efficient-CNN and pre-efficient-CNN blocks for dataset diminution and improvement blocks. The unique efficient-CNN is specially designed according to the model proposed by ASCNN (application) CNN-specific) to perform unidirectional and transverse feature extraction and tumor and pixel classification. The recommended Full-ReLU activation feature halves the number of cores in a high-coil filtered winding layer without reducing process quality. In this specific efficient-CNN consists of 8 convolutional layers and 110 kernels. The experiment results were done using the MR brain database from the Arizona university, including eluding with and without tumor images. The proposal model achieved an accuracy of 97.2% to 98%, which proves the efficiency of the model and its ability to assist in the early diagnosis of brain tumors with sufficient accuracy to support the doctors' decision during diagnosis.
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    EPILEPTIC EEG SIGNALS RHYTHMS ANALYSIS IN THE DETECTION OF FOCAL AND NON-FOCAL SEIZURES BASED ON OPTIMISED MACHINE LEARNING AND DEEP NEURAL NETWORK ARCHITECTURE
    (World Scientific, 2023-06) Saminu, Sani; Xu, Guizhi; Zhang, Shuai; Abd El Kader, Isselmou; Jabire, Adamu Halilu; Ahmed, Yusuf Kola; Karaye, Ibrahim Abdullahi; Ahmad, Isah Salim
    Objective: Most studies in epileptic seizure detection and classi¯cation focused on classifying di®erent types of epileptic seizures. However, localization of the epileptogenic zone in epilepsy patient brain's is paramount to assist the doctor in locating a focal region in patients screened for surgery. Therefore, this paper proposed robust models for the localization of epileptogenic areas for the success of epilepsy surgery. Method: Advanced feature extraction techniques were proposed as e®ective feature extraction techniques based on Electroencephalogram (EEG) rhythms extracted from Fourier Basel Series Expansion Multivariate Empirical Wavelet Transform (FBSE-MEWT). The proposed extracted EEG rhythms of ; ; ; and features were used to obtain a joint instantaneous frequency and amplitude components using a subband alignment approach. The features are used in Sparse Autoencoder (SAE), Deep Belief Network (DBN), and Support Vector Machine (SVM) with the optimized capability to develop three new models: 1. FMEWT–SVM 2. FMEWT SAE–SVM, and 3. FMEWT–DBN–SVM. The EEG signal was preprocessed using a proposed Multiscale Principal Component Analysis (mPCA) to denoise the noise embedded in the signal. Main results: The developed models show a signi¯cant performance improvement, with the SAE–SVM outperforming other proposed models and some recently reported works in literature with an accuracy of 99.7% using -rhythms in channels 1 and 2. Signi¯cance: This study validates the EEG rhythm as a means of discriminating the embedded features in epileptic EEG signals to locate the focal and non-focal regions in the epileptic patient's brain to increase the success of the surgery and reduce computational cost.
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    Investigation of Optimal Components and Parameters of the Incremental PCA-based LSTM Network for Detection of EEG Epileptic Seizure Events
    (Faculty of Science, Gombe State University (GSU), Nigeria, 2023-12) Saminu, Sani; Jabire, Adamu Halilu; Aliyu, Hajar Abdulkarim; Yahaya, Suleiman Abimbola; Iliyasu, Adamu Yau; Ibitoye, Morufu Olusola; Xu, Guizhi
    Prediction of Epileptic seizures is highly imperative to improve the epileptic patient’s life. Epileptic seizures occur due to brain cells excessive abnormal activity that leads to unprovoked seizures and may occur without prior notice. Therefore, preventive measure that monitor and alert the possible occurrence of the seizures is paramount. Commercial and clinical available epileptic seizure computer aided detection system that utilized deep learning algorithms suffers from many challenges. These challenges ranges from low accuracy and precision, sensitive to artifacts and noise, among others. To enhance and increase the accuracy and optimal performance of these networks, this paper endeavor to investigate various optimization algorithm to optimized the network components and parameters in the developed incremental Principal Components Analysis based Long Short-Term Memory (Inc-PCA-LSTM) network for the detection and classification of Electroencephalograph (EEG) epileptic seizure signals based on the big data scenario. The model proved to be effective in the characterization of seven seizure events. The Adam, Elu, Orthogonal, and L1/L2 performed better than their counterparts in optimization functions, activation functions, initialization functions, and regularisation techniques respectively. The accuracy values of 97.5%, 97.5%, 98.4%, and 98.5% was obtained for each of the mentioned core components receptively.
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    Multi-Classification of Electroencephalogram Epileptic Seizures Based on Robust Hybrid Feature Extraction Technique and Optimized Support Vector Machine Classifier
    (Istanbul University Cerrahpasa, 2023-08) Saminu, Sani; Xu, Guizhi; Zhang, Shuai; Abd El Kader, Isselmou; Aliyu, Hajara Abdulkarim; Jabire, Adamu Halilu; Ahmed, Yusuf Kola; Ahmad, Isah Salim
    Epilepsy is a disease with various forms. However, limited dataset has confined classification studies of epilepsy into binary classes only. This study sort to achieve multiclassification of epileptic seizures through a robust feature extraction technique by comprehensively analyzing various advanced feature parameters from different domains, such as energy and entropy. The values of these parameters were computed from the coefficients of dilation wavelet transform (DWT) and modified DWT, known as dual-tree complex wavelet transform decomposition. The model was evaluated from the features of each of the parameters. The hybrid features were divided into three experiments to extract the meaningful features as follows: 1). features from combined energy features were extracted; 2). features from combined entropy features were also extracted; and 3). features from combined parameters as hybrid features were extracted. Finally, the model was developed based on the extracted features to perform a multi-classification of seven types of seizures using an optimized support vector machine (SVM) classifier. A recently released temple university hospital corpus dataset consisting of long-time seizure recordings of various seizures was employed to evaluate our proposed model. The proposed optimized SVM classifier with the hybrid features performed better than other experimented models with the value of accuracy, sensitivity, specificity, precision, and F1-score of 96.9%, 96.8%, 93.4%, 95.6%, and 96.2%, respectively. The developed model was also compared with some recent works in literature that employed the same dataset and found that our model outperformed all the compared studies.
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    A Novel Computer Aided Detection System for Detection of Focal and Non-Focal EEG Signals using Optimized Deep Neural Network
    (IEEExplore, 2021-12) Saminu, Sani; Xu, Guizhi; Zhang, Shuai; Abd El Kader, Isselmou; Jabire, Adamu Halilu; Ahmed, Yusuf Kola; Aliyu, Hajara Abdulkarim; Adamu, Mohammed Jajere; Iliyasu, Adamu Yau; Umar, Faiza Ali
    Epilepsy is a neurological disorder affecting people of all ages. This disorder is reported to affect over 50 million people, with a majority residing in developing countries [1]. It is a sudden and unprovoked seizure that occurs due to an erratic change in the brains' electrical activity often accompanied by loss of consciousness, uncontrolled motions, jerking, and loss of memory [2] [3]. These inconvenient and undesirable effects undermine the quality of life of epilepsy patients as it's difficult for patients and doctors to predict when and where these seizures would occur. Therefore, it is highly imperative to develop an automated system for monitoring epileptic seizures and to assist clinicians in proper and efficient diagnosing of this disease [4] [5].