Biomedical Engineering: Applications, Basis and Communications

Malaria is a life-threatening mosquito-borne disease. Recently, the number of malaria cases has increased worldwide, threatening vulnerable populations. Malaria is responsible for a high rate of morbidity and mortality in people all around the world. Each year, many people, die from this disease, according to the World Health Organization (WHO). Thick and thin blood smears are used to determine parasite habitation and computer-aided diagnosis (CADx) techniques using machine learning (ML) are being used to assist. CADx reduces traditional diagnosis time, lessens socio-economic impact, and improves quality of life. This study develops a simplified model with selective features to reduce processing power and further shorten diagnostic time, which is important to resource-constrained areas. To improve overall classification results, we use a decision tree (DT)-based approach with image pre-processing called optimal features to identify optimal features. Various feature selection and extraction techniques are used, including information gain (IG). Our proposed model is compared to a benchmark state-of-art classification model. For an unseen dataset, our proposed model achieves accuracy, precision, recall, F-score, and processing time of 0.956, 0.949, 0.964, 0.956, and 9.877 s, respectively. Furthermore, our proposed model’s training time is less than those of the state-of-the-art classification model, while the performance metrics are comparable.

The most significant issue with diabetes is diabetic retinopathy (DR), which is the primary cause of blindness. DR typically develops no symptoms at the beginning of the disease, thus numerous physical examinations, including pupil dilation and a visual activity test, are necessary for DR identification. Due to the differences and challenges of DR, it is more challenging to identify it during the manual assessment. For DR patients, visual loss is prevented thanks to early detection and accurate therapy. Therefore, it is even more necessary to classify the severity levels of DR in order to provide a successful course of treatment. This study develops a deep learning method based on chronological rider sea lion optimization (CRSLO) for the classification of DR. The segmentation process divides the image into multiple subgroups, which is necessary for the appropriate detection and classification procedure. For the efficient identification of DR and classification of DR severity, the deep learning approach is used. Additionally, the CRSLO scheme is used to train the deep learning technique to achieve higher performance. With respect to testing accuracy, sensitivity, and specificity of 0.9218, 0.9304 and 0.9154, the newly introduced CRSLO-based deep learning approach outperformed other existing DR classification techniques like convolutional neural networks (CNNs), deep convolutional neural network (DCNN), synergic deep learning (SDL), HPTI-V4 and DR$|$GRADUATE. The Speech Enhancement Generative Adversarial Network (SEGAN) model in use also produced increased segmentation accuracy of 0.90300.

The detection of sleep is important because it contributes to most road accidents especially high levels of deep sleep while driving. Sleep detection is based on electrooculogram (EoG) signal as sleep causes various changes to this signal. Drivers travelling for long hours, especially those working under transportation field are more likely to sleep in the middle of their journey. In order to avoid this situation, drivers are aided with a system which is capable of monitoring the drivers’ condition depending on communication between the driving simulator and the subject EoG signal as many sleep detection devices are dependent upon eye behavior and movement in addition to pupil size and eye closure for certain periods. Therefore, to solve the problem of detecting sleep while driving, this work extracted different features from the EoG signal precisely from its frequency range (0–25Hz) and (25–37.5Hz) by discrete wavelet transform technique. In this research, 15 subjects have been set in a driving environment for more than 1h for collecting the sleep EoG signal data by low power sensors. The EoG signal is recorded using Cobra3 Data acquisition set and few features (minimum, maximum, mean, standard deviation (SD), mode, energy, median and variance) are extracted using discrete wavelet transform. These features have been used to classify three classes (sleep 0, sleep 0, sleep 1) using support vector machine (SVM). This classifier depends upon the fusion of the above features to get an accuracy of 78% for high-level sleep detection based on db4 wavelet.

The neuroscience field provides extensive knowledge regarding cerebral activity principles. Therefore, it enables congregating consumer information and anticipating its preferences. Unlike classical marketing techniques, for instance, interviews with consumers, in which they usually do not communicate their real preferences, biomedical methodologies provide more powerful tools such as electroencephalogram signals and brain imaging, to explore the activity within the brain and examine its miscellaneous responses, which contribute efficiently to understanding human behavior related to its purchasing decision-making. Aiming to highlight the impact of neuroscience on marketing advancement, we first present in this paper a thoughtful background based on state-of-the-art studies to investigate the rate of several neurology techniques’ contribution to the advancement of the marketing field and their effect on purchasing decision-making. Second, we propose a predictive modeling framework based on the analysis of EEG signals recorded during decision-making in terms of “like” or “dislike” of specific consumer products. The discrete wavelet transform (DWT) and kNN classifier were combined to develop such an automated model. For evaluation purposes, the developed model was performed on a well-known and public EEG dataset collected for marketing studies. Achieving promising results confirms that the developed framework can be used as a reliable tool for market strategy development.

Diabetes mellitus (DM) is a multifaceted disease that leads to higher cardiovascular events with neuronal damage, inflammation, and oxidative stress in subjects. It also causes an autonomic imbalance with the onset of the disease which disturbs the cardiac dynamics. This work demonstrates the rutin in treating the inflammation caused by hyperglycemia through nonlinear heart rate variability features in predicting diabetes using a support vector machine (SVM). The lead-I electrocardiogram was acquired from the control, experimental, and treated group of the male Wister rats ($200\pm 20$ gm and age 10–12 weeks). A dataset of 669 samples was obtained from the recorded ECG signal and taken as input vectors to the SVM. The observed results presented an accuracy of 92.9% in classifying the control and experimental group. Further, the same model with the treated group dataset showed an accuracy of 7.7% (samples nearer to the experimental group) while 92.3% of samples were close to the control group. The findings suggested the efficacy of rutin drugs in restoring the blood sugar level and the sympathovagal balance. The usefulness of the non-invasive technique in the prognosis of the disease gives direction in the design and development of the computer-aided cost-effective wearable system. However, the need for expert clinicians cannot be ignored.

This study aims to develop a patient-specific hip implant for osteoarthritis conditions and to compare with intact and conventional implant. The femoral bone with head and shaft region was segmented from the pelvic griddle and converted into 3D model. The parameters such as femoral ball diameter, shaft length, acetabular cup diameter, and neck angle were measured from the segmented 3D model. In this study, designed part of hip implant was assembled together to form a customized hip implant. The von Mises stress was measured by means of Finite element analysis (FEA) method by applying various forces applied at the distal end of hip implant. The forces applied at hip implant were based on the assumption of 500 N force for standing, 2000 N force for walking, and 3000 N force for jogging condition. The minimum stress attained at the femur bone of custom-model is 1.32 MPa for 500 N loading condition, 5.3 MPa for 2000 N and 7.96 MPa for the maximum load of 3000 N. Thus the customized model experienced better stress distribution compared to conventional model under the maximum load of 3000 N. In pelvic region, the custom model attained a lower stress of 23% compared to conventional model. Thus, the study recommends the customized hip implants for the osteoarthritis conditions to avoid revision surgery.

Tissue valve in combination with a mechanical valve is predominantly used in stented valvular prostheses. Porcine pericardium (PP) is a promising xenograft in addition to the predominately used porcine aortic valve (PAV) and bovine pericardium (BP) in heart valve replacement. Tissue valves are structurally similar to the valve cusps, upon fixation they function as structural and functional units to restore the failing heart valves. In this paper, the characterization, design and performance of PP based prosthetic mitral leaflets are analyzed. Uniaxial tensile test was performed to characterize glutaraldehyde (GA)-treated PP and evaluate its mechanical properties. Finite element methods were instrumental to design and analyze the performance of PP leaflets. Different geometric parameters were analyzed to obtain ideal valve performance. Since geometrical parameters influence valve performance, two leaflet models of trileaflet and quadrileaflet configuration were studied. BP and PAV leaflet models were designed and analyzed as controls to compare the performance of PP. The stress distribution, bending momentum and coaptation pattern from the finite element determine the performance of the geometrical models. PP exhibited anisotropy, promising tensile strength and pliability. A thinner porcine pericardium with promising tensile strength and pliability is ideal for the development of low-profile prosthetic valves. The quadrileaflet model exhibited.

With the continuous innovation and development of materials science and tissue engineering technology, biomaterials are gradually being used to replace autologous bone and allogeneic bone grafts in clinical practice for the treatment and repair of bone defects. In this paper, L-arginine-modified chitosan/hydroxyapatite composites were prepared by solution blending of L-arginine-modified chitosan and hydroxyapatite. A hormone drug prednisone acetate was loaded into the composites by supercritical impregnation technique. The results of in vitro release showed that under the loading condition of 12 MPa and 318 K, the cumulative release amount of the drug was 74.6% in 72 h, which had an excellent sustained release effect. In addition, a numerical model of the nasal bone was developed and numerical calculations were performed to analyze and compare the stresses of the healthy nasal bone and the nasal bone repaired with CA/HA composite when subjected to a force of 100 N, in different directions. The total deformation difference at the material was 0.002-0.004 mm/mm, and the stress difference was 0.004–1.373 MPa for the nasal bone in both states, with the sagittal plane under 0–90 degrees of force. The above results indicate that the CA/HA composite has good biological and mechanical properties and can be used to repair nasal bone defects. This material and numerical calculation method can also be applied to other related bone tissue engineering and biomedical materials, which have broad application prospects.