Biomedical Engineering: Applications, Basis and Communications

Sit-to-stand (STS) motion is one of the most common movements of a person during daily life. The motion, as a prerequisite for other movements, such as running and climbing, can be a very challenging task for the elderly or disabled people. Assistant tools, such as auxiliary walkers and exoskeletons, are commonly used to help these individuals have independent or at least semi-independent life. In order to design efficient rehabilitation treatments and also develop assistive devices for STS motion, it is necessary to have a validated dynamic model with an appropriate embedded controller on the ancillary instrument to reach the desired posture of the human body. In this paper, a linkage-based dynamic model of STS motion using Lagrange’s equation is extracted. The dynamic of the model is simulated using anthropometric parameters associated with the subject. Moreover, two robust controller types are designed and implemented in the presence of sinusoidal bounded disturbances in the dynamic system on the model in order to track the desired trajectory acquired from experimental kinematic data. The simulation results of the controlled dynamic model are compared with experimental data from a healthy subject and demonstrate the performance of the controller. The proposed model can be utilized in the design and control of an assistance device for STS motion.

Medical industry reports prostate cancer as common and high among men and alarms the necessity for detecting prostate cancer for which the required morphology is extracted from the histopathology images. Commonly, the Gleason grading system remains a perfect factor for grading prostate cancer in men, but pathologists suffer from minute inter- and intra-observer variations. Thus, an automatic method for segmenting and classifying prostate cancer is modeled in this paper. The significance of the developed method is that the segmentation and classification are gland-oriented using the Color Space (CS) transformation and Salp Swarm Optimization Algorithm-based Rider Neural Network (SSA-RideNN). The gland region is considered as the morphology for cancer detection from which the maximal significant regions are extracted as features using multiple-kernel scale-invariant feature transform (MK-SIFT). Here, the RideNN classifier is trained optimally using the proposed Salp–Rider Algorithm (SRA), which is the integration of Salp Swarm Optimization Algorithm (SSA) and Rider Optimization Algorithm (ROA). The experimentation is performed using the histopathology images and the analysis based on sensitivity, accuracy, and specificity reveals that the proposed prostate cancer detection method acquired the maximal accuracy, sensitivity, and specificity of 0.8966, 0.8919, and 0.8596, respectively.

Measuring oxygen saturation of blood (SpO2) clinically plays a vital role in patient’s health monitoring. In fact, monitoring oxygen level is necessary for people having respiratory problems (pulmonary hypertension) and in other critical conditions. The primary motivation of this work is to develop a low cost computer-based oxygen saturation monitoring system using an embedded system along with lab windows CVI platform. The process of calculating the level of oxygen saturation in the blood using non-invasive method is also called as pulse oximetry, which consists of LED and photo detectors, using MSP430FG4618 microcontroller. The MSP430 employed in designing the microcontroller firmware program for digitization and transmission of the data from sensor to the computer. NI-based Lab windows/CVI Platform was developed as a part of this project to receive, plot, save data and determine the accuracy of SpO2 value. In this proposed system, we have achieved the maximum accuracy of 99.49% which is better than the previously developed methods. The proposed system is also designed with the low cost and low power consuming modules.

Objective: The antinuclear antibodies (ANA) that present in the human serum have a link with various autoimmune diseases. Human Epithelial type-2 (HEp-2) cells acts as a substance in the Indirect Immuno fluorescence (IIF) test for diagnosing these autoimmune diseases. In recent times, the computer-aided diagnosis of autoimmune diseases by the HEp-2 cell classification has drawn more interest. Though, they often pose limitations like large intra-class and small inter-class variations. Hence, various efforts have been performed to automate the procedure of HEp-2 cell classification. To overcome these problems, this research work intends to propose a new HEp-2 classification process.

Materials and Methods: This is regulated by integrating two processes, namely, segmentation and classification. Initially, the segmentation of the HEp-2 cells is carried out by deploying the morphological operations. In this paper, two morphology operations are deployed called opening and closing. Further, the classification process is exploited by proposing a modified Convolutional Neural Network (CNN). The main objective is to classify the HEp-2 cells effectively (Centromere, Golgi, Homogeneous, Nucleolar, NuMem, and Speckled) and is made by exploiting the optimization concept. This is implanted by developing a new algorithm called Distance Sorting Lion Algorithm (DSLA), which selects the optimal convolutional layer in CNN.

Results: Through the performance analysis, the performance of the proposed model for test case 1 at learning percentage 60 is 3.84%, 1.79%, 6.22%, 1.69%, and 5.53% better than PSO, FF, GWO, WOA, and LA, respectively. At 80, the performance of the proposed model is 5.77%, 6.46%, 3.95%, 3.24%, and 5.55% better from PSO, FF, GWO, WOA, and LA, respectively. Hence, the performance of the proposed work is proved over other models under different measures.

Conclusion: Finally, the performance is evaluated by comparing it with the other conventional algorithms in terms of accuracy, sensitivity, specificity, precision, FPR, FNR, NPV, MCC, F1-Score and FDR, and proves the efficacy of the proposed model.

Objectives: In this study, we assess the possible influence of soybean isoflavone (genistein) and other flavonoids (quercetin and catechin) on breast cancer chemoprevention. We design in-vitro and in-vivo experiments to analyze the effect of genistein, quercetin and catechin on cell proliferation, cell migration, and angiogenesis of breast cancer cells.

Methods: In cell proliferation experiment, MCF-7 cells, SKBR-3 cells, and HUVEC cells were treated with genistein and other flavonoids (catechin and/or quercetin) for 48h to assess the influence on cell growth of normal and breast cancer cells. In cell motility test, we analyze the effect of isoflavone and flavonoids on migration ability of MCF-7 cells by 16h and SKBR-3 cells by 24h in two different concentrations (1.25$\mu$g/ml and 2.5$\mu$g/ml). In the in-vivo experiment, SKBR-3 cells mixed with PBS and catechin, respectively, were injected subcutaneously into nude mice, then we investigated the effect of catechin on cell growth by observing subcutaneous tumor size changes after 15 days.

Results: The results suggest that genistein and quercetin can significantly inhibit proliferation of breast cancer cells, and their inhibitory effects are independent of estrogen receptor. In cell motility tests, all of the three phytochemicals were effective in the inhibition of cell migration on two breast cancer cell lines, except for quercetin on cell migration of SKBR-3 cell line. In the in-vitro experiment, catechin showed stimulatory effect on cell proliferation of HUVEC cell line, which may consider positive effect on angiogenesis, rather than inhibitory effect. However, in the in-vivo experiment, it showed no significant change in tumor size between the groups of with and without catechin treatment.

Conclusions: According to our study, the results suggest that isoflavone and flavonoids tend to inhibit cell growth and metastasis of breast cancer cells. Our in-vivo experiment does not reach a significant result, and it may be due to lower catechin concentration. Under in-vivo environment, we should also consider the possible metabolic forms of catechin that cause different result from the in-vitro study.

In this paper, we propose an automated approach that combines the generalized discriminant analysis (GDA) as feature reduction scheme with radial basis function (RBF) kernel and the online sequential extreme learning machine (OSELM) having Sigmoid, Hardlim, RBF and Sine activation function as binary classifier for detection of congestive heart failure (CHF) and coronary artery disease (CAD). For this analysis, 13 nonlinear features as Correlation Dimension (CD), Detrended Fluctuation Analysis (DFA) as DFA-$\alpha$1 and DFA-$\alpha$2, Bubble Entropy (BBEn), Sample Entropy (SampEn), Dispersion Entropy (DISEn), Lempel–Ziv Complexity (LZ), Sinai Entropy (SIEn), Improved Multiscale Permutation Entropy (IMPE), Hurst Exponent (HE), Permutation Entropy (PE), Approximate Entropy (ApEn) and Standard Deviation (SD1/SD2) were extracted from Heart Rate Variability (HRV) signals. For validation of proposed method, HRV data were obtained from standard database of normal sinus rhythm (NSR), CHF and CAD subjects. Numerical experiments were done on the combination of database sets such as NSR-CAD, CHF-CAD and NSR-CHF subjects. The simulation results show a clear difference in combination of database sets by using GDA having RBF, Gaussian kernel function and OSELM binary classifier having Sigmoid, RBF and Sine activation function and achieved an accuracy of 98.17% for NSR-CAD, 100% for NSR-CHF and CAD-CHF subjects.

Modification of Ti–6Al–4V alloys with silver-loaded TiO₂ nanotubes was investigated. In this study, TiO₂ nanotube (TiNT) was grown on the surface of Ti–6Al–4V plates by means of anodization in an electrolyte solution containing glycerol, water and 0.5wt.% of NH₄F. Silver particles were deposited on TiNT using a Photo-Assisted Deposition (PAD) method. Formation of crystalline phase of TiO₂ on the surface was confirmed by means of XRD while its superficial morphology was observed using FESEM/EDS. Hydrophilicity was assessed by means of contact angle measurement. As-synthesized silver-loaded TiNT on osteoblast ATCC growth in vitro was also investigated in terms of its capacity in supporting osseointegration. The cell viability was determined by MTT (3-[4,5-dimethylthiazol-2yl]-2,5diphenyl-2H-tetrazolium bromide) assay and its differentiation activity was measured by alkaline phosphatase (ALP) assay. The results showed that desposition of silver on TiNT increased cell viability after 14 days culture while improving the hydrophilicity feature. Silver-loaded TiNT on Ti–6Al–4V alloy with Ag precursor concentration of 0.10M showed the optimum viability of osteoblast growth, with 14% improvement in comparison to its unmodified counterpart. The MTT assay showed that no cytotoxicity in vitro was observed on this material. This study provides corroborating evidences that the modification of Ti–6Al–4V alloy may enhance the cell viability and its prominence as dental implant materials.

People with serious movement disabilities due to neurodegenerative diseases have problems in their communication with others. Considerable numbers of communication aid systems have been developed in the past. Especially, some of the systems driven by eye movements are thought to be effective for such people. Electrooculographic (EOG) signal reflects the eye movement and the specific pattern of eye movement can be seen in EOG signals. This paper proposes a communication aid system by extracting the features of EOG. The system consists of a computer, analog-to-digital converter, biological amplifier and two monitors. Two monitors, one for a system user and the other for other people, display the same information. Five items are presented in the monitor, and a user selects those items according to the situation in the communication. Selection of the items is done by combining three eye movements: gaze at left, gaze at right and successive blinks. Basic concept of the communication aid system was designed by taking into account the current state of a subject’s movement disability. Then, the design of a screen and the algorithm for detecting eye movement pattern from EOG were determined by using the data of normal healthy subjects. The system worked almost perfectly for normal healthy subjects. Then, the developed system was operated by a subject with serious movement disability. Parts of the system operation were regarded as satisfactory level, and some miss-operation were also seen.