Browsing by Author "Azar A.T."

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Adaptive neuro-fuzzy system as a novel approach for predicting post-dialysis urea rebound
(2011) Azar A.T.; Department of Electrical Communication and Electronics; Systems Engineering; Modern Science and Arts University (MSA); 26 July Mehwar Road Intersection with Wahat Road; 6th of October City; Egypt
Total dialysis dose (Kt/V) is considered to be a major determinant of morbidity and mortality in haemodialysed patients. The continuous growth of the blood urea concentration over the 30-60-min period following dialysis, a phenomenon known as urea rebound, is a critical factor in determining the true dose of haemodialysis (HD). The misestimation of the equilibrated (true) postdialysis blood urea or equilibrated Kt/V results in an inadequate HD prescription, with predictably poor clinical outcomes for the patients. The estimation of the equilibrated post-dialysis blood urea (C eq) is therefore crucial in order to estimate the equilibrated (true) Kt/V. Measuring post-dialysis urea rebound (PDUR) requires a 30- or 60-min post-dialysis sampling, which is inconvenient. This paper presents a novel technique for predicting equilibrated urea concentration and PDUR in the form of a Takagi-Sugeno-Kang fuzzy inference system. The advantage of this neuro-fuzzy hybrid approach is that it does not require 30-60-min post-dialysis urea sample. Adaptive neuro-fuzzy inference system (ANFIS) was constructed to predict equilibrated urea (C eq) taken at 60 min after the end of the HD session in order to predict PDUR. The accuracy of the ANFIS was prospectively compared with other traditional methods for predicting equilibrated urea (C eq), PDUR and equilibrated dialysis dose ( eqKt/V). The results are highly promising, and a comparative analysis suggests that the proposed modelling approach outperforms other traditional urea kinetic models. � 2011 Inderscience Enterprises Ltd.
Artificial neural network for prediction of equilibrated dialysis dose without intradialytic sample.
(2011) Azar A.T.; Wahba K.M.; Electrical Communication & Electronics Systems Engineering department; Modern Science and Arts University (MSA); 6th of October City; Egypt.
Post-dialysis urea rebound (PDUR) is a cause of Kt/V overestimation when it is calculated from pre-dialysis and the immediate post-dialysis blood urea collections. Measuring PDUR requires a 30-or 60-min post-dialysis sampling, which is inconvenient. In this study, a supervised neural network was proposed to predict the equilibrated urea (C eq) at 60 min after the end of hemodialysis (HD). Data of 150 patients from a dialysis unit were analyzed. C eq was measured 60 min after each HD session to calculate PDUR, equilibrated urea reduction rate eq (URR), and ( eq Kt/V). The mean percentage of true urea rebound measured after 60 min of HD session was 19.6 10.7. The mean urea rebound observed from the artificial neural network (ANN) was 18.6 13.9%, while the means were 24.8 14.1% and 21.3 3.49% using Smye and Daugirdas methods, respectively. The ANN model achieved a correlation coefficient of 0.97 (P <0.0001), while the Smye and Daugirdas methods yielded R = 0.81 and 0.93, respectively (P <0.0001); the errors of the Smye method were larger than those of the other methods and resulted in a considerable bias in all cases, while the predictive accuracy for ( eq Kt/V) 60 was equally good by the Daugirdas' formula and the ANN . We conclude that the use of the ANN urea estimation yields accurate results when used to calculate ( eq Kt/V).
Biomedical Engineering: Specialisations and future challenges
(2011) Azar A.T.; Electrical Communication and Electronics Systems Engineering Department; Modern Science and Arts University (MSA); 26 July Mehwar Road intersection with Wahat Road; 6th of October City; Egypt
Biomedical Engineering applies the principles of engineering, biology, and medicine to create devices and methods that solve problems in the health care industry. Due to the diversity of the field, it has become essential to identify tracks or specialisations that students can engage in during their final years of study. However, due to the limited resources, especially in the Arab world, most universities stick to limited specialisations. In this paper, we shall review the state of the art tracks in Biomedical Engineering globally with concentration on the Arab World. We shall also focus on the prospect of the field and the expected future requirements. 2011 Inderscience Enterprises Ltd.
Neuro-fuzzy system for cardiac signals classification
(2011) Azar A.T.; Electrical Communication and Electronics Systems Engineering Department; Modern Science and Arts University (MSA); Mehwar Road intersection with Wahat Road; 6th of October City; Egypt
The classification of the electrocardiogram (ECG) into different patho-physiological disease categories is a complex pattern recognition task. This paper presents an intelligent diagnosis system using hybrid approach of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electrocardiogram (ECG) signals. Wavelet-transform is used for effective feature extraction and ANFIS is considered for the classifier model. It can parameterise the incoming ECG signals and then classify them into eight major types for health reference: left bundle branch block (LBBB), normal sinus rhythm (NSR), pre-ventricular contraction (PVC), atrial fibrillation (AF), ventricular fibrillation (VF), complete heart block (CHB), ischemic dilated cardiomyopathy (ISCH) and sick sinus syndrome (SSS). The inclusion of adaptive neuro-fuzzy interface system (ANFIS) in the complex investigating algorithms yields very interesting recognition and classification capabilities across a broad spectrum of biomedical problem domains. The performance of the ANFIS model is evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the ECG signals. Cross validation is used to measure the classifier performance. A testing classification accuracy of 95% is achieved which is a significant improvement. Copyright � 2011 Inderscience Enterprises Ltd.
A novel system for haemodialysis efficiency monitoring
(2011) Azar A.T.; Electrical Communication and Electronics Systems Engineering Department; Modern Science and Arts University (MSA); 26 July Mehwar Road intersection with Wahat Road; 6th of October City; Egypt
A novel system dynamics (simulation) model is developed to evaluate the effect of dialysis policies on session performance, quantify, optimise dialysis efficiency and monitor dialysis performance online. The developed system focuses on analysing and highlights factors which may alter the delivered dose and may lead to session degradation This will help increase the achievement of adequate haemodialysis to a level consistent with or higher than national adequacy statistics, in order to reduce the morbidity rate of the haemodialysis patient. The simulation results and the statistical analysis revealed that there is no statistically significant difference between the calculated results and the measured results. This system dynamics model is considered the novel system that calculates the dialysis session performance as a function of not only dialysis adequacy but also the intradialytic complications and overall equipment effectiveness. Copyright � 2011 Inderscience Enterprises Ltd.
Overview of biomedical engineering
(IGI Global, 2013) Azar A.T.; Modern Science and Arts University; Egypt
Biomedical Engineering is a branch that unites engineering methods with biological and medical sciences in order to enhance the quality of our lives. It focuses on understanding intricate systems of living organisms, and on technology development, algorithms, methods, and advanced medical knowledge, while enhancing the conveyance and success of clinical medicine. With engineering principles, biomedical engineering improves the procedures and devices to overcome health care and medical problems by combining both biology and medicine with engineering principals. In the field of Biomedical Engineering, engineers usually need to have background knowledge from such different fields of engineering as electronics, mechanical, and chemical engineering. Specialties in this field like bioinstrumentation, biomechanics, biomaterials, medical imagining, clinical engineering, bioinformatics, telemedicine and rehabilitation engineering, which will be introduced in this chapter together with an overview of the field of biomedical engineering. � 2013, IGI Global.
System dynamics as a useful technique for complex systems
(2012) Azar A.T.; Department of Electrical Communication and Electronics Systems Engineering; Modern Science and Arts University (MSA); 6th of October City; Egypt
System dynamics (SD) is a powerful methodology and computer simulation modelling technique for framing, understanding and discussing complex issues and problems. It is widely used to analyse a range of systems in, e.g. business, ecology, medical and social systems as well as engineering. The methodology focuses on the way one quantity can affect others through the flow of physical entities and information. Often such flows come back to the original quantity causing a feedback loop. The behaviour of the system is governed by these feedback loops. There are two important advantages of taking systems dynamics approach. The interrelationship of the different elements of the systems can be easily seen in terms of cause and effects. Thus the true cause of the behaviour can be identified. The other advantage is that it possible to investigate which parameters or structures need to be changed in order to improve behaviour. This paper deals with the design of a framework for SD models and gives an overview of the current SD simulation packages. Copyright � 2012 Inderscience Enterprises Ltd.