Browsing by Author "Ahmed, Yusuf Kola"

Now showing 1 - 14 of 14
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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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    Design and Construction of a Portable Electronic Sleep Inducer for Low Resource Settings
    (Faculty of Engineering, FUOYE, Nigeria, 2020-09) Ahmed, Yusuf Kola; Zubair, Abdul R.; Saminu, Sani; Akande, Kareem A.; Afolayan, Mubarak A.; Afonja, Awawu A.
    Good quality restful sleep is indispensable to mental and physical health. However, pressure due to busy life style, work and sometimes physiological factors have placed constraints on adequate and healthy sleep pattern leading to several sleep disorders such as insomnia, sleep apnea and restless leg syndrome. Sleep disorder affects the quality of life of such patients as it grossly reduces efficiency at work and leads to poor mental and physical health. Available drugs to treat this disorder are addictive with strong adverse effects, while existing devices to provide intervention are very expensive. Hence, the development of an affordable, portable electronic sleep inducer with display unit is presented. It uses geomagnetic property of the earth coupled with electromagnetic wave induction to stimulate sleep. The signal frequency was generated by IC4047 coupled with Arduino Uno and ATmega 328p for device control. The output of this electronic sleep inducer is found to satisfactorily produce 5.89 Hz and 3.58 Hz for theta and delta waves respectively, needed to induce sleep. It consumes less power and it is rechargeable.
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    Design and Development of a Hybrid Eye and Mobile Controlled Wheelchair Prototype using Haar cascade Classifier: A Proof of Concept
    (Springer Nature Switzerland, 2023-08) Ahmed, Yusuf Kola; Suleiman, Taofik Ahmed; Saminu, Sani; Danmusa1, Nasir Ayuba; Salahudeen, Kafilat Atinuke; Zubair, Abdul Rasak; Adelodun, Abdulwasiu Bolakale
    According to the wheelchair foundation, about 1.86% of the world’s population requires a functional wheelchair. Most of these wheelchairs have manual control systems which puts millions of people with total paralyzes (total loss of muscle control including the head) at a disadvantage. However, the majority of those who suffer from muscular and neurological disorders still retain the ability to move their eyes. Hence the concept of eye-controlled wheelchair. This paper focused on the design and development of a hybrid control system (eye and mobile interface) for a wheelchair prototype as a proof of concept. The systemwas implemented using the pre-trained Haar cascade ML classifier in open CV. Focus was shifted from high accuracy common to lab-based studies to deployment and power consumption which are critical to usability. The system consists of a motor chassis that takes the place of a wheelchair, a raspberry pi4 module which acts as a mini-computer for image and information processing, and a laser sensor to achieve obstacle avoidance. The Bluetooth module enables serial communication between the motor chassis and the raspberry pi, while the power supply feeds the raspberry pi and the camera. The system performance evaluation was carried out using obstacle avoidance and navigation tests. An accuracy of 100% and 89% were achieved for obstacle avoidance and navigation, respectively, which shows that the system would be helpful for wheelchair users facing autonomous mobility issues.
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    Design and Development of an Inexpensive Footswitch Controlled Surgical Light Prototype for Low Resource Healthcare Setting
    (Faculty of Technology Education, Abubakar Tafawa Balewa University Bauchi, 2021-03) Ahmed, Yusuf Kola; Saminu, Sani; Opeyemi, Mustapha; Balogun, Zainab Ovayoza
    Surgical procedures and patient examination activities have been identified as critical universal health care components by the 2015 world health assembly. These critical tasks often require adequate lighting supply for successful execution. However, surgical lightings are very expensive for rural health care centres in middle and low-income countries. Besides, the epileptic grid power supply has rendered the few available ones underutilized. Technically, most of these lighting designs' control mechanism tends to interfere with the concentration of the surgeon or supporting staff during procedures. Hence, an inexpensive surgical light with footswitch control and battery bank is proposed. The prototype was implemented using 85% locally sourced materials without compromising standards in line with sustainable development goals. The device passed the Chassis leakage test as well as mechanical stability tests. On illumination tests, the device performed seamless control tasks without distraction. It produces a luminosity of 8500lx and correlated colour temperature of 6000k at an average cost of 109 USD.
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    DESIGN OF A LEUKEMIA DETECTION SYSTEM USING DIGITAL BLOOD SMEAR IMAGES
    (Faculty of Engineering and Technology, University of Ilorin, 2023-06) Saminu, Sani; Muniru, I.O.; Suleiman Abimbola, Yahaya; Oladimeji, A. J.; Ajibola, T.M.; Ibitoye, M.O.; Ahmed, Yusuf Kola; Jilantikiri, L. J.
    Leukaemia is a fatal blood cancer that occurs due to the formation of abnormal and excessive increases in white blood cells in the bone marrow or blood. The traditional approaches used to diagnose the disease involve the manual analysis of blood sample images obtained from a microscope. This approach is tedious, slow, timeconsuming, and prone to errors. Therefore, automatic detection of leukaemia based on the counting of the two blood cells is paramount for diagnosis and increasing the patient’s survival rate. This paper presents a system that can detect each of the two blood cells needed through image processing, segmentation, and classification. The detection, classification, and counts are only limited to two of the cells present in the digital blood smear which are the white blood cells (WBCs) and red blood cells (RBCs). The model was evaluated with a collection of confirmed cases and normal cases to test its effectiveness in predicting the presence of Leukaemia by computing the ratio of WBC to RBC. The suggested model exhibits good performance results and can be utilized to make a reliable computer-aided diagnosis detection of leukaemia cancer.
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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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    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].
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    PERFORMANCE ANALYSIS OF TRANSMIT DIVERSITY CONFIGURATIONS BASED ON OSTBC ALAMOUTI’S EXTENSION
    (Department of Electrical Engineering, Ahmadu Bello University, Zaria, 2021-03) Saminu, Sani; Jabire, Adamu Halilu; Abdulkarim, Abubakar; Ahmed, Yusuf Kola; Iliyasu, Adamu Yau; Salisu, Sani; Karaye, Ibrahim Abdullahi; Ahmad, Isah Salim
    One of the diversity configurations is transmit diversity. It is employed to mitigate multipath fading channel in a time varying channels to improve wireless communication system and make it more reliable. This paper presents a review of diversity techniques and configurations with various signal processing techniques and space time coding system that are mostly employed, diversity combining schemes and analysis of diversity schemes is also exploited. We also proposed a robust space time coding scheme based on orthogonal design by extending the Alamouti’s space time block coding to a higher order diversity and evaluates its performance based on signal to noise ratio (SNR) and bit error rate (BER). The advantage of this transmit diversity is to simplify the hardware requirement by providing a cost effective solution in broadband wireless system with eliminating the need for adopting many antennas at the receiver side
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    Performance Comparison of Transmit and Receive Diversity under Rayleigh Faded Channel Using Extended Alamouti’s Scheme
    (Faculty of Technology Education, Abubakar Tafawa Balewa University Bauchi, 2021-03) Saminu, Sani; Jabire, Adamu Halilu; Ahmed, Yusuf Kola; Jajere, Adamu Muhammed; Ahmad, Isah Salim
    Diversity techniques have been used over the years to improve the wireless communication links, mitigate fading, achieve higher data rates, and improve channel capacity gains. This paper presents the comparative analysis of transmitting and receive diversity techniques with our proposed extended Alamouti’s scheme using orthogonal space-time block codes (OSTBC) under the Rayleigh faded channel. In this paper, three possible diversity configurations have been considered: multiple-input multiple-output (MIMO), single-input multiple outputs (SIMO), and multiple-input single-output (MISO). The model was developed in a Matlab environment and performance comparison was carried out using BER vs SNR. Our proposed model proved that the MIMO system is highly efficient in improving wireless communication links. Also, our proposed transmit diversity scheme with a higher number of antenna arrays achieves full diversity as in receive combining schemes
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    Performance of Extended Alamouti’s Scheme Using Orthogonal Space Time Block Codes
    (Department of Electrical Engineering, Ahmadu Bello University, Zaria, 2020-09) Saminu, Sani; Jajere, Adamu Halilu; Abdulkarim, Abubakar; Ahmed, Yusuf Kola; Karaye, Ibrahim Abdullahi; Ahmad, Isah Salim
    In today’s communication system, satisfying users demand is very challenging and difficult due to the increase in multimedia and internet applications requirement within the limited radio spectrum coupled with multipath fading and interference. Signaling techniques that are robust and efficient need to be investigated and developed. Diversity techniques that used multiple antennas such as space time wireless technology has been proposed to improve the wireless communication in a multipath fading, interference, and signal scattering wireless links. This paper proposed an extended Alamouti’s scheme based on orthogonal space time block code (OSTBC). This proposed scheme is aimed to improve the wireless system efficiency in multipath fading links technique, also it improves the Multiple input multiple output (MIMO) Raleigh fading channel, and minimizes the bit error ratio (BER). The model is developed in MATLAB environment and performance of the model is analyzed in terms of BER vs Signal to Noise Ratio (SNR).
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    A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal
    (MDPI, 2021-05-20) Saminu, Sani; Guizhi, Xu; Zhang, Shuai; Isselmou, Abd El Kader; Jabire, Adamu Halilu; Ahmed, Yusuf Kola; Karaye, Ibrahim Abdullahi; Ahmad, Isah Salim
    The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.
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    Reduction of Mutual Coupling in UWB/MIMO Antenna Using Stub Loading Technique
    (Sciendo, 2021) Jabire, Adamu Halilu; Abdu, Anas; Saminu, Sani; Sadiq, Abubakar Muhammad; Jajere, Adamu Muhammed; Ahmed, Yusuf Kola
    The research presents mutual coupling reduction between UWB-MIMO antenna elements using stub loading technique. The proposed 2 × 2 UWB antenna geometry consists of two circular-shaped monopole radiators with a partial ground for perfect impedance matching. Stubs of 20 mm × 0.2 mm are inserted between the two antenna elements in the ground plane to improve the isolation. The decoupling stub leads to a mutual coupling reduction of less than 20 dB. The farfield measurement at a selected frequency of 10 GHz confirms an omnidirectional radiation pattern. Different MIMO antenna metric such as channel capacity loss (CCL), mean effective gain (MEG), total active reflection coefficient (TARC), envelope correlation coefficient (ECC), and surface current are presented. Details of the design considerations and the simulation and measurement results are presented and discussed. The proposed MIMO antenna array can be well suited for UWB applications.