Biomedical Engineering: Applications, Basis and Communications

A computational framework, for the numerical approximation of the exothermic reaction of ethylene oxide (EO) with water, to form ethylene glycol is presented in this paper. Ethylene Glycol also known as Mono-ethylene Glycol (MEG), is a diol with a boiling of 198${}^{\circ }$C and conventionally produced through hydrolysis of ethylene oxide which is obtained through the oxidation of ethylene. It is used as an excellent automobile coolant as the 1:1 ratio mixture of MEG with Water boils at 129${}^{\circ }$C and freezes at $-37^{\circ }$C. Other than its use as an antifreeze, it is also used as a reagent during the production of polyester fibers, pharmaceutics, cosmetics, hydraulic fluids, printing inks, explosives, polyesters and paint solvents. The mathematical model presented here, consists of an energy balance and a material balance system, described in an axisymmetric coordinate system. The optimized resulting values using the artificial intelligence approach are summarized in this paper. We derive an analytical solution. The analytical solution for the mathematical model equations is in general not possible for this model but it may be possible to derive an analytical solution to this mathematical model if we consider the equation for the conservation of material (chemical species) as a formulation for plug flow and isothermal conditions. Noteworthy findings are reported in this paper for future research.

An electrocardiogram (ECG) signal is an important diagnostic tool for cardiologists to detect the abnormality. In continuous monitoring, an ambulatory huge amount of ECG data is involved. This leads to high storage requirements and transmission costs. Hence, to reduce the storage and transmission cost, there is a requirement for an efficient compression or coding technique. One of the most promising compression techniques is Compressive Sensing (CS) which makes efficient compression of signals. By this methodology, a signal can easily be reconstructed if it has a sparse representation. This paper presents the Block Sparse Bayesian Learning (BSBL)-based multiscale compressed sensing (MCS) method for the compression of ECG signals. The main focus of the proposed technique is to achieve a reconstructed signal with less error and more energy efficiency. The ECG signal is sparsely represented by wavelet transform. MIT-BIH Arrhythmia database is used for testing purposes. The Huffman technique is used for encoding and decoding. The signal recovery is appropriate up to 75% of compression. The quality of the signal is ascertained using the standard performance measures such as signal-to-noise ratio (SNR) and Percent root mean square difference (PRD). The quality of the reconstructed ECG signal is also validated through the visual method. This method is most suitable for telemedicine applications.

One of the complications caused by the viral agent SARS-CoV2 is atypical pneumonia that occurs classically in viral pathologies. These infection complications produce a sort of “cytokine release syndrome” that sees interleukin 6, a glycosylated protein of approximately 212 amino acids, among the leading players in the inflammatory process. IL-6 typically produces a transient inflammatory state that promotes the host’s immune defence through its pleiotropic function. There is the stimulation of a response in the acute infectious phase, hematopoiesis and the regular advent of immune reactions. The action of the anti-inflammatory cytokines, which tends to regulate the inflammatory one’s activity, is directed to the same cells that produce IL-6, which, through an inhibition mechanism, slow down or production ceases altogether. Evidently, in the case of the IL-6 storm, the action of these anti-inflammatory cytokines is insufficient, and the blockade of IL-6R receptors and through the use of monoclonal antibody-like tocilizumab has proved to be optimal to manage complications and avoid potentially fatal situations. Therefore, the purpose of this paper is to create a mathematical model that describes the action of the IL-6 cytokine in SARS-CoV2 virus infection to understand better the extent of the disease itself and the associated severe side effects. We represent the concentration of tocilizumab, soluble IL-6R, absolute neutrophils and circulating platelets using computational modeling. Tocilizumab is administered by intravenous infusions with a minimum dose of 80mg and a maximum dose of 400mg. Following tocilizumab administration, simulation results indicate that the population of absolute neutrophils and circulating platelets is decreasing. After the removal of tocilizumab concentration, both absolute neutrophils and circulating platelets return at their baselines.

Age-related macular degeneration (AMD) is an eye disease that affects the elderly. AMD’s prevalence is increasing as society’s population ages; thus, early detection is critical to prevent vision loss in the elderly. Arrangement of a comprehensive examination of the eye for AMD detection is a challenging task. This paper suggests a new poly scale and dual path (PSDP) convolutional neural network (CNN) architecture for early-stage AMD diagnosis automatically. The proposed PSDP architecture has nine convolutional layers to classify the input image as AMD or normal. A PSDP architecture is used to enhance classification efficiency due to the high variation in size and shape of perforation present in OCT images. The poly scale approach employs filters of various sizes to extract features from local regions more effectively. Simultaneously, the dual path architecture incorporates features extracted from different CNN layers to boost features in the global regions. The sigmoid function is used to classify images into binary categories. The Mendeley data set is used to train the proposed network and tested on Mendeley, Duke, SD-OCT Noor, and OCTID data sets. The testing accuracy of the network in Mendeley, Duke, SD-OCT Noor, and OCT-ID is 99.73%,96.66%,94.89%,99.61%, respectively. The comparison with alternative approaches showed that the proposed algorithm is efficient in detecting AMD. Despite having been trained on the Mendeley data set, the proposed model exhibited good detection accuracy when tested on other data sets. This shows that the suggested model can distinguish AMD/Normal images from various data sets. As compared to other methods, the findings show that the proposed algorithm is efficient at detecting AMD. Rapid eye scanning for early detection of AMD could be possible with the proposed architecture. The proposed CNN can be applied in real-time due to its lower complexity and less learnable parameters.

The reflected wave of Photoplethysmography (PPG) is generated due to impedance mismatch, when the pulse wave propagates along the arterial branches. The objective of the study is to estimate the reflected wave amplitude and duration from PPG and to determine whether it changes with age. Reflected wave of PPG was modeled using the lognormal model fitting method. The duration and amplitude of reflected wave and augmentation index from modeled PPG was compared with the Slope Sum Function (SSF) and second derivative of PPG. PPG was collected from 120 subjects with age $<$40 and $>$40 years old for carrying out this experiment. Reflected wave duration from modeled PPG and SSF method shows a mean square error value of $0.63\pm 0.29$ and $0.94\pm 0.8$ for subjects $<$40 and $>$40 years age, respectively. The amplitude of reflected wave from PPG model and Second Derivative of Photolethysmography (SDPPG) results in a mean square error of $2.31\pm 2.08$ and $2.32\pm 0.36$ for subjects $<$40 and $>$40 years age, respectively. Augmentation index calculated from PPG model and SDPPG was similar with good level of agreement in the scatter plot. The modeled PPG helps to identify the reflected wave parameter noninvasively and the results show a considerable variation in these parameters with age.

This study investigates the effect of variable viscosity (exponential and linear) and constant reabsorption for the urine flow through a narrow tube. The inertial free flow of viscous fluid has been governed by the momentum and mass conservation through the cross-section of axisymmetric tube. The governing partial differential equations have been simplified with the help of stream function and stress components with exponential and linear variable viscosity. The resulting partial differential equations have been solved by the inverse method and give the explicit expressions for velocity, pressure, shear stress, flux and leakage of flow. It has been observed that flow in transverse direction increases with the increase in reabsorption velocity at wall, whereas horizontal flow, shear stress and volume flow rate become slow with the increase in uniform reabsorption velocity. Effect of viscosity is significant near the walls of tube because the axial velocity accelerates by increasing viscosity parameter due to the pressure gradient near the center of tube but it decelerates near the walls of tube due to surface friction. Also, the special case of variable viscosity is discussed by assuming the linear type of viscosity. The derived data for the velocity and flow rate have been used to measure the fractional reabsorption in proximal tube with varying viscosity near the wall.

The sympathetic and parasympathetic function of the Autonomic Nervous System (ANS) is the primary cause of the variations in Heart Rate and Skin Conductance (SC) during different physical activities.

This paper aims to analyze the effect of different physical activities i.e. (a) Supine (b) Standing and (c) Warm-up, on Heart Rate Variability (HRV) and SC. The standard dataset of 18 subjects has been used to analyze the effect of physical activities on the HRV and SC. In the used dataset, the subjects are in supine, standing, and warm-up positions. The linear methods (time domain & frequency domain) of HRV are implemented on the standard dataset for analyzing the effect of physical activities. It has been observed with the analysis of the HRV that the mean value of time domain methods i.e. the NN interval’s standard deviation (SDNN), the successive RR interval’s root mean square (RMSSD), RR intervals with more than 50 ms differences in between them (NN50), percentage of successive RR intervals that have the difference of more than 50 ms (pNN50) are decreased and the value of Heart rate (HR) increased when the activity has been changed from supine to standing and standing to the warm-up positions. The value of frequency domain methods, such as low frequency (LF) and the ratio of low and high frequency (LF/HF) increased, while the value of HF decreases as activity changes from supine to standing and from supine to warm-up position. Further, the increment is also observed in the value of SC when activity is switched from supine to standing and from standing to the warm-up position. It is concluded from the results that there is a significant decrement that is observed in the value of HRV, while the increment is observed in the value of SC and HR. Decrement of HRV reflects that the sympathetic activity is increased as activity changed from supine to standing and further from standing to warm-up positions.

Due to the drastic fluctuation of glucose level in diabetic patients throughout a day, especially when they take a sweet breakfast, and greasy lunch and dinner, their insulin must be promptly measured invasively after each meal. However, invasive measurement of the blood glucose level (BGL) is painful and might lead to infection. In this paper, we design a hardware–software system for estimating BGL in a real-time manner, according to the amount of near-infrared (NIR) absorption at 950 nm. The received light wave is amplified and passed through a low-pass filter (LPF) to eliminate the irrelevant frequency components. We have executed our experiment according to three scenarios: (i) Certain amount of glucose is dissolved in water and the glucose concentration is measured by our equipment, (ii) 5-cm³ blood sample of participants is taken and the BGL is measured in a test tube by our apparatus and then this sample is moved to the gold-standard equipment and the actual value of BGL is measured. The measured voltage versus BGL in our designed optical equipment indicates a fairly linear relation between them. (iii) BGL is measured over the fingertip and compared to the gold-standard value. In this case, the measurement error does not exceed 25% and the voltage change with respect to the BGL shows a nonlinear behavior. The designed system has a low cost and provides an acceptable error, making it suitable to be commercialized for diabetic patients.