Browsing by Author "Karaye, Ibrahim Abdullahi"

Now showing 1 - 10 of 10
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    Analysis of Cardiac Beats using Higher Order Spectra
    (IEEE, 2014-10-29) Karaye, Ibrahim Abdullahi; Saminu, Sani; Özkurt, Nalan
    For early diagnosis of the heart failures, the electrocardiography (ECG) is the most common method because of its simplicity and cost. Computer based analysis of ECG provides reliable and efficient tools in diagnostics of arrhythmias. With this objective there are lots of studies on automatic and semi-automatic ECG analysis. Like many biosignals, ECG signals are nonlinear in nature, higher order spectral analysis (HOS) is known to be a very good tool for the analysis of nonlinear systems producing good noise immunity. Thus in this study, HOS analysis of ECG signals of normal heart rate, right bundle branch block, paced beat, left bundle block branch and atrial premature beats have been studied in order to reveal the complex dynamics of ECG signals using the tools of nonlinear systems theory. Some of the general characteristics for each of these classes in the bispectrum and bicoherence plot for visual observation have been presented. For the extraction of RR intervals, well known Pan-Tompkins algorithm has been used and three higher order statistical parameters of skewness, kurtosis and variance from these features have been computed. These features with statistical parameters fed into artificial neural network classifier (ANN) and obtained an average accuracy of 94.9%.
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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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    Electroencephalogram (EEG) Based Imagined Speech Decoding and Recognition
    (institution of Applied Materials and Technology Society with the cooperation of Faculty of Engineering, Universitas Riau, Pekanbaru, Indonesia, 2021-05-25) Saminu, Sani; Guizhi, Xu; Zhang, Shuai; Isselmou, Abd El Kader; Jabire, Adamu Halilu; Karaye, Ibrahim Abdullahi; Ahmad, Isah Salim; Abdulkarim, Abubakar
    ĉe recent investigations and advances in imagined speech decoding and recognition has tremendously improved the decoding of speech directly from brain activity with the help of several neuroimaging techniques that assist us in exploring the neurological processes of imagined speech. ĉis development leads to assist people with disabilities to beneėt from neuroprosthetic devices that improve the life of those suffering from neurological disorders. ĉis paper presents the summary of recent progress in decoding imagined speech using Electroenceplography (EEG) signal, as this neuroimaging method enable us to monitor brain activity with high temporal resolution, it is very portable, low cost, and safer as compared to other methods. ĉerefore, it is a good candidate in investigating an imagined speech decoding from the human cortex which remains a challenging task. ĉe paper also reviews some recent techniques, challenges, future recommendations and possible solutions to improve prosthetic devices and the development of brain computer interface system (BCI).
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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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    Hybrid Feature Extraction Technique for Multi-Classification of Ictal and Non-Ictal EEG Epilepsy Signals
    (Universiti Teknologi Malaysia (UTM), 2020-08-29) Saminu, Sani; Guizhi, Xu; Zhang, Shuai; Isselmou, Abd El Kader; Jabire, Adamu Halilu; Karaye, Ibrahim Abdullahi; Ahmad, Isah Salim
    These Electroencephalography (EEG) signals is an effective tool for identification, monitoring, and treatment of epilepsy, but EEG signals need highly experienced personnel to interpret it correctly due to its complexity, even for an expert it is monotonous and usually consume much time. Therefore, the automatic computer-aided device (CAD) needs to be developed to overcome those challenges associated with epilepsy interpretation and diagnosis. The system efficiency relies largely on the quality of features supply as input to classifiers. This paper presents an efficient feature extraction technique to develop a CAD system that can detect and classify normal, interictal and ictal epilepsy signals correctly with high accuracy. Our approach employs time-frequency features, statistical features and nonlinear features combined as hybrid features to train and test the classifier. Machine learning classifiers of multi-class support vector machine (mSVM) and feed-forward neural network (FFNN) with fivefold cross-validation are used to classifies normal, interictal and ictal with our proposed features. Our system was tested using a publicly available database with three classes each of 100 single channels EEG signals of 4096 samples point each. Based on sensitivity, specificity, and accuracy, our proposed approach of multiclass classification shows a good performance with 96.7%, 98.3% and 100% of sensitivity, specificity, and accuracy respectively.
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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 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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    Updates on Movie Recommendation System
    (Faculty of Technology Education, Abubakar Tafawa Balewa University Bauchi, 2021-02) Musa, Jamilu Maaruf; Zhihong, Xu; Saminu, Sani; Muswelu, Cecillia; Karaye, Ibrahim Abdullahi; Ahmad, Isah Salim
    In recent years, there is a huge number of movies on the internet. Users have different desires for a movie to watch as there are different cultures, languages, and genres to choose from in a movie domain. As a result, a recommendation system approach is used to suggest the best movies to users according to their preferences. Several different algorithms and strategies have been proposed to effectively capture users’ interest and provide an accurate recommendation of movies. Memory-Based Collaborative Filtering Recommender Systems existed for the best part of the last two decades. It is an advanced technology, implemented in various commercial applications which because of its effectiveness has been the predominantly used technique to date in recommendation system. Memory-based collaborative filtering approach is popularly and extensively used in practice but yet faces some key challenges in providing high-quality recommendations due to the daily increase of items and visitors of different websites. This paper presents a review of different techniques and similarity measures used in the movie recommendation system and also proposed a model that can be used to build robust, accurate and scalable movie recommendation to users.
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    Wavelet feature extraction for ECG beat classification
    (IEEE, 2014-10-29) Saminu, Sani; Özkurt, Nalan; Karaye, Ibrahim Abdullahi
    Electrocardiography (ECG) signal is a bioelectrical signal which depicts the cardiac activity of the heart. It is a technique used primarily as a diagnostic tool for various cardiac diseases. ECG provides necessary information on the electrophysiology and changes that may occur in the heart. Due to the increase in mortality rate associated with cardiac diseases worldwide despite recent technological advancement, early detection of these diseases is of paramount importance. This paper has proposed a robust ECG feature extraction technique suitable for mobile devices by extracting only 200 samples between R-R intervals as equivalent R-T interval using Pan Tompkins algorithm at preprocessing stage. The discrete wavelet transform (DWT) of R-T interval samples are calculated and the statistical parameters of wavelet coefficients such as mean, median, standard deviation, maximum, minimum, energy and entropy are used as a time-frequency domain feature. The proposed hybrid technique has been tested by classifying three ECG beats as normal, right bundle branch block (Rbbb) and paced beat using the signals from Massachusetts Institute of Technology Beth Israel Hospital (MIT-BIH) arrhythmia database and processed using Matlab 2013 environment. Classification has been performed using neural network backpropagation algorithm because of its simplicity. While equivalent R-T interval features gives average accuracy of 98.22%, the proposed hybrid method gives a promising result with average accuracy of 99.84% with reduced classifier computational complexity.