Browsing by Author "Suleiman Abimbola Yahaya"

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    Development of an Electrically Powered Medical Suction Device for Clinical Applications in Developing Countries
    (JOURNAL OF SCIENCE TECHNOLOGY AND EDUCATION, 2024) Morufu Olusola Ibitoye; Olufunke Mary Oderemi; Suleiman Abimbola Yahaya
    In clinical settings, the process of suction is the removal of biological fluids using vacuum technology. This study sought to design and develop an inexpensive suction device for the facilitation of airway management in patients under emergency or critical care. The suction device was fabricated based on the design specifications. The device was designed to be able to aspirate biological samples at low (70 mmHg), medium (90 mmHg), and high (120 mmHg) pressures using catheters of 9.4 mm and 3.3 mm diameters. These pressures were selected to enable the device to be useful for infants and elderly patients. The developed device passed the required electrical safety tests using the standard electrical safety analyzer by Fluke. For example, the leakage AC and DC were 0.1 µA AC and 0.0 µA DC, respectively, suggesting that the device is safe for use on patients. We are confident that the introduction of this inexpensive device (
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    Investigation of Optimal Components and Parameters of the Incremental PCA-based LSTM Network for Detection of EEG Epileptic Seizure Events
    (Bima Journal of Science and Technology, 2024) Sani Saminu; Adamu Halilu Jabire; Hajara Abdulkarim Aliyu; Adamu Ya’u Iliyasu; Suleiman Abimbola Yahaya; Morufu Olusola Ibitoye; Guizhi Xu
    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.