Browsing by Author "Adamu, Mohammed Jajere"

Now showing 1 - 5 of 5
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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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    A crossed-polarized four port MIMO antenna for UWB communication
    (Elsevier, 2022-12-29) Jabire, Adamu Halilu; Salisu, Sani; Saminu, Sani; Adamu, Mohammed Jajere; Hussein, Mousa I.
    This paper presents a compact, crossed-polarized, ultra-wideband (UWB) four-ports multiple- input-multiple-output (MIMO) printed antenna. The proposed antenna is constructed from four microstrip circular patch elements fed by a 50-Ω microstrip line. Two metamaterial cell elements, in the form of a rectangular concentric double split ring resonator (SRR), are placed at the upper plane of the substrates for bandwidth improvement and isolation enhancement. The ultra- wideband frequency response is achieved using a defective ground plane. Surface current flow between the antenna’s four elements is limited to ensure maximum isolation. The four-port MIMO system is designed with orthogonal antenna elements orientation on an FR4 substrate with a loss tangent of 0.02 and an overall size of 30 mm ×30 mm ×1.6 mm. Such orientation resulted in less than 􀀀17dB port-to-port isolation and an impedance bandwidth of 148% (3.1–12 GHz). The proposed UWB-MIMO antenna achieved a maximum realized gain of 6.2dBi with an efficiency of 87%. The measured and simulated results are in good agreement over the operating frequency band (3.1–12 GHz). The results also provide overall good diversity performance with the TARC <􀀀10 dB, ECC <0.001, DG >9.9, MEG <􀀀3 dB and CCL <0.1. The proposed antenna is well- suited for applications in WLAN, WIMAX and GPRs.
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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].
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    Performance Analysis of Turbo Codes for 5G Massive Machine-Type Communication(mMTC)
    (IEEExplore, 2021-12) Adamu, Mohammed Jajere; Qiang, Li; Zakariyya, Rabiu Saleh; Jabire, Adamu Halilu; Kukawa, Halima Bello; Saminu, Sani
    In our present and future wireless communication systems, high-performance codes with low design complexity are required for optimum coding gain. In this paper, an efficient Long Term Evolution (LTE) based Turbo decoding algorithm is proposed. it is derived by remodeling the conventional maximum a posteriori probability (MAP) decoder. The proposed scheme aims to reduce the complexity of actualizing the conventional MAP Turbo decoder in the newly mMTC PHY layer features. The overall system performance is analyzed in terms of bit error rate (BER).
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    Unraveling the pathophysiology of schizophrenia: insights from structural magnetic resonance imaging studies
    (Frontier, 2023-05-19) Adamu, Mohammed Jajere; Qiang, Li; Nyatega, Charles Okanda; Younis, Ayesha; Kawuwa, Halima Bello; Jabire, Adamu Halilu; Saminu, Sani
    Background: Schizophrenia affects about 1% of the global population. In addition to the complex etiology, linking this illness to genetic, environmental, and neurobiological factors, the dynamic experiences associated with this disease, such as experiences of delusions, hallucinations, disorganized thinking, and abnormal behaviors, limit neurological consensuses regarding mechanisms underlying this disease. Methods: In this study, we recruited 72 patients with schizophrenia and 74 healthy individuals matched by age and sex to investigate the structural brain changes that may serve as prognostic biomarkers, indicating evidence of neural dysfunction underlying schizophrenia and subsequent cognitive and behavioral deficits. We used voxel-based morphometry (VBM) to determine these changes in the three tissue structures: the gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). For both image processing and statistical analysis, we used statistical parametric mapping (SPM). Results: Our results show that patients with schizophrenia exhibited a significant volume reduction in both GM and WM. In particular, GM volume reductions were more evident in the frontal, temporal, limbic, and parietal lobe, similarly the WM volume reductions were predominantly in the frontal, temporal, and limbic lobe. In addition, patients with schizophrenia demonstrated a significant increase in the CSF volume in the left third and lateral ventricle regions. Conclusion: This VBM study supports existing research showing that schizophrenia is associated with alterations in brain structure, including gray and white matter, and cerebrospinal fluid volume. These findings provide insights into the neurobiology of schizophrenia and may inform the development of more effective diagnostic and therapeutic approaches.