Mashhour M. Bani Amer

An accurate amperometric glucose sensor based glucometer with eliminated cross-sensitivity

This paper represents an experimental analysis of cross-sensitivity, which is the main problem that usually faces amperometric glucose sensors. The elimination of cross-sensitivity is based on experimental determination and verification of new correction factors. The correction factors obtained were then used to design a new glucometer with cross-sensitivity of the amperomtric glucose sensor to urea concentration in human blood eliminated. The accuracy of this glucometer satisfies the clinical requirements and the results of glucose measurements obtained using this glucometer are highly correlated with the glucose measurements for the same blood samples obtained using the well-known Yellow Springs Instrument glucose analyser.

A new intelligent approach for estimation of blood potassium concentration

This paper introduces a new approach for estimation of blood potassium concentration. This approach is based on a neurofuzzy inference system that combines the attributes of both fuzzy logic and neural networks. This approach has many attractive clinical features. First, it represents a computerised intelligent method for accurately estimating blood potassium without the need for a potassium sensor. Second, it helps the clinicians in diagnosis and treatment of potassium disorders and also reduces the time required to deal with them. Third, it enhances the patients comfort and compliance due to a significant reduction in blood sample volume required to conduct the electrolytes analysis. Lastly, the developed approach explains the complex physiological homeostasis of blood electrolytes which is very important for the design of decision-making systems for medical applications. Furthermore, the validation results of this method showed that it is capable of estimating the blood potassium with an accuracy satisfying clinical standards.

Novel Design of Low Noise Preamplifier for Medical Ultrasound Transducers

A novel design of low noise amplifier for medical ultrasound transducers is described in this paper. Unlike conventional low noise preamplifiers, this design proposes a new circuit configuration which has electronically adjustable matching resistance that allows the preamplifier to be compatible with a variety of medical ultrasound transducers. The design employs current feedback operational amplifier to enhance the gain-bandwidth independence and improve the design slew rate. Simulation results show that the proposed design has very low output noise voltage spectral density and the level of this noise does not increase when its tunable matching resistance is increased or decreased.

Fuzzy-based framework for diagnosis of acid–base disorders

The main objective of this research is to develop a fuzzy-based framework for diagnosis of different acid-base disorders. There are several acid-base disorders that cause many clinical complications and their proper diagnosis is the only way for their efficient treatment. The common disorders are metabolic acidosis, metabolic alkalosis, non-anion gap acidosis, anion-gap acidosis, acute respiratory alkalosis and chronic respiratory alkalosis. The proposed fuzzy-based framework was used to diagnose all of these disorders using four parameters directly measured in blood: hydrogen-ion concentration (pH), arterial blood carbon dioxide partial pressure (paCO₂), sodium ions concentration (Na⁺) and chloride ions concentration (Cl⁻) along with 12 features extracted from the directly measured parameters. The validation results showed that the developed framework has an accuracy of 94%, an average sensitivity of 88% and a specificity of 93%. These results imply that the developed fuzzy-based framework is accurate and reliable one and can be used to help clinicians specially the non-expert ones to provide correct and rapid diagnosis of acid-base disorders.

Mathematical model of chloride concentration in human blood

This paper deals with mathematical modelling of blood chloride concentration. The main features of the model are that it reveals mathematically the physiological relationship between blood chloride and other electrolytes and serves as an accurate indirect method for chloride measurements with accuracy fulfilling clinical requirements. The main advantages of the method based on this model are that it is more comfortable than traditional methods and clinically less harmful for the patient under study. Experimental verification of the developed model ensures that the results of chloride measurements obtained using this model are significantly correlated with the results for the blood samples obtained from standard chloride analysers.

Design of a user-friendly LabVIEW-based toolbox for real-time monitoring and diagnosis of vital signals

This paper introduces a powerful LabVIEW-based toolbox for monitoring, diagnosis and management of vital biosignals [electrocardiogram (ECG) and electromyogram (EMG) ]. The main features of this software present in user-friendly graphical user interface, the patients data management, real-time monitoring of heart and muscles activities, reviewing patients progress through variety of charts, diagnosis of heart and muscles disorders and data transmission to remote clinician. The experimental validation of this toolbox showed that it has good performance and demonstrated a superior assurance quality in optimising clinical decisions based on real-time analysis of patients vital signals.

An adaptive neurofuzzy technique for determination of blood acidity

This paper presents an adaptive neurofuzzy-based method for the determination of blood acidity (pH). The main advantage of this method in comparison with conventional ones used for blood pH measurements is that it is capable of estimating the blood pH without the need for a pH sensor, which in turn reduces the volume of blood sample required to conduct the chemical analysis. This method uses blood carbon dioxide partial pressure (pCO2) and bicarbonate ( ) as input to a neurofuzzy approach to predict the value of blood pH. This method was validated using 60 test data points that had not been used during the training process. The obtained results showed that the pH values predicted using this method have good concordance with experimentally measured pH values. The high correlation coefficient (87.6%) between the measured and the predicted pH values reflects the methods ability to measure (estimate) the pH values using pCO2 and with accuracy satisfying clinical demands.This paper presents an adaptive neurofuzzy-based method for the determination of blood acidity (pH). The main advantage of this method in comparison with conventional ones used for blood pH measurements is that it is capable of estimating the blood pH without the need for a pH sensor, which in turn reduces the volume of blood sample required to conduct the chemical analysis. This method uses blood carbon dioxide partial pressure (pCO₂) and bicarbonate (HCO⁻₃) as input to a neurofuzzy approach to predict the value of blood pH. This method was validated using 60 test data points that had not been used during the training process. The obtained results showed that the pH values predicted using this method have good concordance with experimentally measured pH values. The high correlation coefficient (87.6%) between the measured and the predicted pH values reflects the methods ability to measure (estimate) the pH values using pCO₂ and HCO⁻₃ with accuracy satisfying clinical demands.

The role of neuro-fuzzy modelling as a greening technique, in improving the performance of vehicular spark ignition engine

The spark ignition engine, by far, is the largest source of motive power in the world. Therefore, continuous endeavours to improve its performance are needed to save in fuel consumption and reduce cost. The main goal of this paper is to develop a neuro-fuzzy model for fuel Injection Time (IT) in order to design a neuro-fuzzy controller for improving the performance of the spark ignition engine. The obtained results showed that the developed neuro-fuzzy model is capable of predicting the fuel IT with a mean squared error less than 0.0072. Furthermore, the power produced by the neuro-fuzzy controller has higher values of about 15-73% than the power produced by the PID controller used in the basic engine. The BSFC is reduced by about 2-5% compared to the PID controller.

Fuzzy Approach for Determination the Optimum Therapeutic Parameters in Neuromuscular Stimulation Systems

This paper presents a fuzzy system for determination the optimum therapeutic parameters (frequency, amplitude and session duration) in neuromuscular stimulation systems. The developed approach has many clinical benefits and features. These include: capability to determine and adjust the therapeutic parameters online during any treatment session, reduction of time and number of sessions needed to overcome the neuromuscular disorders, preventing patient from fatigue or pain that may occur during or after treatment, considering differences between various patients and improving the efficiency of the available neuromuscular stimulation systems.

Development of a web-based healthcare information system

This paper introduces a web-based healthcare information system (WB-HIS). The developed HIS consists of secure web server that stores the patient’s database, internet infrastructure, review station (client PC) and client personal digital assistant (PDA). The proposed system has many attractive features. First, the WB-HIS provides a global communication and thus satisfies the new requirements of modern HIS to shift from hospital-centred HIS to a global one to be accessed from anywhere and at anytime. Second, the WB-HIS helps the physicians to increase their medical outcomes because they can see more number of patients during his day time and access remotely the patient’s information which is clinically high demanded especially in case of emergencies. Finally, it facilitates real-time patient assessment and analysis during examination and diagnosis which improves the healthcare service.