Browsing by Author "Aliyu, Hajara Abdulkarim"

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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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    COMPARISON OF DEEP LEARNING ALEXNET AND SUPPORT VECTOR MACHINE TO CLASSIFY SEVERITY OF SICKLE CELL ANEMIA
    (Gombe State University, 2022-08) Aliyu, Hajara Abdulkarim; IBRAHIM, MUHAMMAD JAMILU; Saminu, Sani; MUHAMMAD, FATIMA ABDULLAHI
    Sickle cell anemia (SCA) is a serious hematological blood disorder, where affected patients are frequently hospitalized throughout a lifetime. Most of the patient's life span reduced, and some become addict based on the nature of strong analgesic that is taken by the concern patients, which they all have strong side effects. The existing method of severity classification for SCA patient is done manually through a microscope which is time-consuming, tedious, prone to error, and require a trained hematologist. The affected patient has many cell shapes that show important biomechanical characteristics of patient severity level. The main purpose of the study is to develop an automated severity level classification method of SCA patients by comparing deep learning AlexNet and Support Vector Machine (SVM) to enable present the percentage of each cell present in blood smear image. Hence, having an effective way of classifying the abnormalities present in the SCA disease based on the level of patient severity to give a better insight into managing the concerned patient's life. The study was performed with 182 SCA patients (over 11,000 single RBC images) with 14 classes of abnormalities and a class of normal cells to develop a shape factor quantification and general multiscale shape analysis to classify the patient based on severity level. As a result, it was found that the proposed framework can detect 85.4% abnormalities in SCA patient blood smear in automated manner when compared with Support Vector Machine (SVM) method with 71.9%. Hence, the system classifies the severity of SCA patient automatically and reduce the time and eye stress with performance AlexNet model performance of 95.1% accuracy, 99.1% specificity, and 98.5% precision value.
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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].