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.

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 method's ability to measure (estimate) the pH values using pCO₂ and HCO⁻₃ with accuracy satisfying clinical demands.

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 patient's 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.

In this paper, a neural fuzzy system for the diagnosis of potassium disturbances is presented. This paper develops an adaptive neuro-fuzzy expert system that can provide accurate diagnosis of potassium disturbances. The proposed diagnostic approach has many attractive features. First, it provides an efficient tool for diagnosis of K+ disturbances and aids clinicians, especially the non-expert ones, in providing fast and accurate diagnosis of K+ disturbances in critical time. Second, it significantly reduces the time needed to accomplish precise diagnosis of K+ disturbances and thus enhances the healthcare standards. Third, it is capable of diagnosing the different types of potassium disturbances using a hybrid neural fuzzy approach. Finally, it has good accuracy (higher than 87%), specificity (100%), and average sensitivity (83%). The performance of the proposed diagnostic system was experimentally evaluated and the achieved results confirmed that the proposed system is efficient and accurate in diagnosing K+ disturbances.

This paper presents a new design for an electrotherapeutic system, characterized by a robust graphical user interface (GUI) design and patient feedback approach in order to guide the electrical stimulation process. This system is based on stimulating the patient by an electric current and analysing the signals generated from his/her body in response to the stimulation effect. The main advantage of this system over the conventional designs is that it is capable of providing self-adjustment of the stimulation parameters during the treatment session to achieve optimal stimulation results that increase the treatment efficiency and reduce the time needed to overcome the patients' impairments. Furthermore, it prevents any harm, pain or fatigue due to online measuring of the fatigue parameters during the treatment session. It is also capable of generating various shapes of stimulation pulses to treat patients with different impairments, and it has a friendly GUI that greatly simplifies patient data management and views the stimulus pulses with their controllable parameters.