Hamed, I., and M. I. Owis, "Automatic Arrhythmia Detection Using Support Vector Machine Based on Discrete Wavelet Transform", Journal of Medical Imaging and Health Informatics: American Scientific Publishers, 2015. AbstractWebsite

Arrhythmia is abnormal electrical activity in the heart bringing about less effective pumping. An abnormally fast electrical signal initiates two problems: (1) the heart pumps too quick; and (2) ventricles are filled with an inadequate amount of blood. On the other hand, an abnormally slow electrical signal pumps a sufficient amount of blood out of the heart but too slow. Arrhythmia is classified by both its location of origin and rate. Some arrhythmias are life-threatening and eventually result in cardiac arrest. Hence, the purpose of this study is to present a robust implementation algorithm to discriminate between normal sinus rhythm and three types of arrhythmia: atrial fibrillation (AF), ventricular fibrillation (VF), and supra ventricular tachycardia (SVT) that were collected from physionet database. This is attained by capturing the main features that contain both frequency and location information of the signal through discrete wavelet transform, followed by principal component analysis on each decomposed level. Features were reduced through statistical analysis as an input to support vector machine with optimized parameters that resulted in overall accuracy of 96.89%.

Owis, M. I., A. H. Abou-Zied, A. - B. M. Youssef, and Y. M. Kadah, "Study of features based on nonlinear dynamical modeling in ECG arrhythmia detection and classification", IEEE Trans on Biomedical. Engineering, vol. 49, no. 7, pp. 733–736, 2002. Abstract

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Ellah, M. A., A. Eldeib, and M. I. Owis, "Accelerating DRR Generation using Fourier Slice Theorem on the GPU", IEEE Engineering in Medicine and Biology conference (EMBC2015): IEEE, 2015. Abstract
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Ellah, M. A., A. Eldeib, and M. I. Owis, "GPU Acceleration for Digitally Reconstructed Radiographs using Bindless Texture Objects and CUDA/OpenGL Interoperability", IEEE Engineering in Medicine and Biology conference (EMBC2015): IEEE, 2015. Abstract
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Ahmed, A. F., M. I. Owis, and I. A. Yassine, "Image Features of Spectral Correlation Function for Arrhythmia Classification", IEEE Engineering in Medicine and Biology conference (EMBC2015): IEEE, 2015. Abstract
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Hammed, N. S., and M. I. Owis, "An Integrated Health Monitoring System", Third International Conference on Advances in Biomedical Engineering (ICABME'15), 2015. Abstract
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Khalaf, A. F., M. I. Owis, and I. A. Yassine, "A novel technique for cardiac arrhythmia classification using spectral correlation and support vector machines", Expert Systems with Applications: Pergamon, 2015. Abstract
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Hamad, M. E. S., A. S. A. Mohamed, and M. I. Owis, Wavelet Analysis of Instantaneous Heart Rate to Discriminate Diabetic Patients, , 2006. Abstract
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Owis, M. I., A. M. Youssef, and Y. M. I. Kadah, Novel Techniques for Cardiac Arrhythmia Detection, , 2001. Abstract
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EH, H., and R. Chen, "Integrating Data-Mining Support into a Brain-Image Data-base Using Open-Source Components", Advances in medical sciences, vol. 53, no. 2, pp. 172–181, 2008. Abstract
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