Browsing by Author "Hassan, Ashraf Mohamed Ali"

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Efficient Data Compression of ECG Signal Based on Modified Discrete Cosine Transform
(Tech Science Press, 14/01/2022) Hassan, Ashraf Mohamed Ali; Alzaidi, Mohammed S; Ghoneim, Sherif S. M; El Nahal, Waleed
This paper introduced an efficient compression technique that uses the compressive sensing (CS) method to obtain and recover sparse electrocardiography (ECG) signals. The recovery of the signal can be achieved by using sampling rates lower than the Nyquist frequency. A novel analysis was proposed in this paper. To apply CS on ECG signal, the first step is to generate a sparse signal, which can be obtained using Modified Discrete Cosine Transform (MDCT) on the given ECG signal. This transformation is a promising key for other transformations used in this search domain and can be considered as the main contribution of this paper. A small number of wavelet components can describe the ECG signal as related work to obtain a sparse ECG signal. A sensing technique for ECG signal compression, which is a novel area of research, is proposed. ECG signals are introduced randomly between any successive beats of the heart. MIT-BIH database can be represented as the experimental database in this domain of research. The MIT-BIH database consists of various ECG signals involving a patient and standard ECG signals. MATLAB can be considered as the simulation tool used in this work. The proposed method's uniqueness was inspired by the compression ratio (CR) and achieved by MDCT. The performance measurement of the recovered signal was done by calculating the percentage root mean difference (PRD), mean square error (MSE), and peak signal to noise ratio (PSNR) besides the calculation of CR. Finally, the simulation results indicated that this work is one of the most important works in ECG signal compression.
Robust and High Accuracy Algorithm for Detection of Pupil Images
(Tech Science Press, 2022-06-17) El Nahal, Waleed; Zaini, Hatim G; Zaini, Raghad H; Ghoneim, Sherif S. M; Hassan, Ashraf Mohamed Ali
Recently, many researchers have tried to develop a robust, fast, and accurate algorithm. This algorithm is for eye-tracking and detecting pupil position in many applications such as head-mounted eye tracking, gaze-based human-computer interaction, medical applications (such as deaf and diabetes patients), and attention analysis. Many real-world conditions challenge the eye appearance, such as illumination, reflections, and occasions. On the other hand, individual differences in eye physiology and other sources of noise, such as contact lenses or make-up. The present work introduces a robust pupil detection algorithm with and higher accuracy than the previous attempts for real-time analytics applications. The proposed circular hough transform with morphing canny edge detection for Pupillometery (CHMCEP) algorithm can detect even the blurred or noisy images by using different filtering methods in the pre-processing or start phase to remove the blur and noise and finally the second filtering process before the circular Hough transform for the center fitting to make sure better accuracy. The performance of the proposed CHM- CEP algorithm was tested against recent pupil detection methods. Simulations and results show that the proposed CHMCEP algorithm achieved detection rates of 87.11, 78.54, 58, and 78 according to Swirski, ExCuSe, Else, and ´ labeled pupils in the wild (LPW) data sets, respectively. These results show that the proposed approach performs better than the other pupil detection methods by a large margin by providing exact and robust pupil positions on challenging ordinary eye pictures.