Biomedical Engineering: Applications, Basis and Communications

The essential role and function of a dressing is to provide an environment that promotes wound healing. In this study, the development and characterization of novel polymer hybrid sponge based on chitosan-Grifola frondosa polysaccharide and cross-linked by freeze-thaw method for possible use in a variety of biomedical application is reported. A water-soluble sulfated derivative G. frondosa polysaccharide was obtained from G. frondosa polysaccharide with 98% of concentrated sulfuric acid. To make effective wound healing accelerator, a chitosan/sulfated G. frondosa polysaccharides hybrid sponge was prepared. To evaluate the wound healing effect, full thickness skin excision was performed on the backs of the rats and then the sponge was applied in the wounds, respectively. After 7 days and 14 days, gross and histological examination was performed. Grossly, untreated control group revealed that the wound had well-defined margin and was covered by crust. The testing group treated with sponge appeared to be nearly completely healed. Histology of each group was well correlated to gross findings. The testing group shows nearly complete regeneration of appendage structure similar to normal in the dermis in contrast to control group with absence and less number of skin appendages. It hints that the sponge can accelerate wound healing. The result indicates that the chitosan/sulfated G. frondosa polysaccharide hybrid sponge is a promising dressing for wound healing.

Recently, recurrent plot (RP) has been used as one of the analysis tools in complex system dynamics. In this paper, we hypothesize that complex features extracted from RP have superiority in discriminating the upper extremity performance in two groups of mulitiple sclerosis (MS) patients without tremor and healthy controls compared to statistical and power spectrum features. We define spiral drawing task for upper extremity and the position signals is recorded from subjects. Then, velocity profiles are extracted and the common statistical and spectral features are exported. To extract complex features from RP, a modified methodological approach based on density distribution is presented and the properties of distribution are calculated as complex features. Finally, the applicability and capabilities of these three groups of features are invested by a Neuro-Fuzzy Classifier. The performance of the Neuro-Fuzzy classifier is reported as sensitivity, specificity and accuracy criteria. The results of the analysis yield out that complex features have the highest performance comparatively. This hypothesis is proven and validated through the experiments and it is shown that the complex features have promising discriminating capabilities. To validate the classifier used, different structures of the neuro-fuzzy classifier are studied in terms of the number of membership functions and the type of fuzzy sets and the most efficient structure is extracted out. Furthermore, the efficiency of the Neuro-fuzzy classifier with its optimum structure and tuned parameters is compared with some other well-known and commonly used classifiers.

Surgical treatment has been proved to be an effective way to control seizures for some kinds of intractable epilepsy. The electrocorticogram (ECoG) recorded from subdural electrodes has become an important technique for defining epileptogenic zones before surgery in clinical practice. The exact location of subdural electrodes has to be determined to establish the connection between electrodes and epileptogenic zones. Artifacts caused by the electrodes can severely affect the quality of CT imaging and sequentially image registration. In this paper, we discussed the performance of mean squares and the Mattes mutual information metric methods in multimodal image registration for subdural electrode localization. Since the skull can be regarded as a rigid body, rigid registration is sufficient for the purpose of subdural electrode localization. The vital parameter for the rigid registration is rotation. The translation result depends on the result of rotation. Both the methods performed well in the determination of the rotation center. Rotation angles of different image pairs of the same volume pair fluctuated a lot. Based on the image acquisition process, we assume that the images within the same volume pair should have the same transformation parameters for registration. Results show that the mean rotation angles of images within one dataset are approximate to the manual results that are considered to be the actual result for registration despite their fluctuation range.

In this study, the cardiovascular system is modeled using an equivalent electronic system focusing on the circle of Willis (COW). COW supports adequate blood supply to the brain. Each cardiovascular artery is modeled using electrical elements such as resistor, capacitor and inductor. MATLAB Simulink software was used to obtain the left and right ventricles pressure as well as pressure distribution at efferent arteries of the COW. Firstly, the normal operation of the system was shown then the stenosis of cerebral arteries was induced in the circuit and the effects were studied. In the normal condition, the difference between pressure distribution of right and left efferent arteries (left and right anterior cerebral arteries (ACA-A2), left and right middle cerebral arteries (MCA), left and right posterior cerebral arteries (PCA-P2) was calculated to see the effect of anatomical difference between left and right sides of supplying arteries of COW. In stenosis cases, the effect of internal carotid artery (ICA) occlusion on efferent arteries pressure was investigated. The model results were verified by comparing to the clinical observation reported in the literature.

This study developed a portable embedded multi-sensor fusion for point-of-care health monitoring to evaluate the accumulated stress levels of affected people. The instrument integrates numerous physiological parameters to quantify the level of accumulated stress, which is one of the leading causes of cardiovascular disease. The participants in this study were assigned daily mental arithmetic tasks over one week to simulate stress-accumulation conditions. Fuzzy logic rules were defined to quantify the accumulated stress level by combining electrodermal activity (EDA) and electrocardiograph (ECG) and photoplethysmograph (PPG) signals. The trends of 21 physiological parameters were analyzed and compared, from which the following four physiological parameters were identified as being representative of the effective response to cumulative stress: (1) the heart rate, (2) the ratio of low- to high-frequency powers for heart rate variability (HRV), (3) the skin conductance level (SCL) and (4) the liver harmonic proportion (i.e. the first harmonic of the peripheral blood volume pulse spectrum). Subsequently, the observed trends of these four parameters were combined to enhance the robustness and the reliability of the proposed system by adjusting the applied fuzzy logic rules. The experimental results show an 82% correlation between the measured level of accumulated stress and the social readjustment rating scale (SRRS) obtained from subject-independent testing.

Human voice intonation is affected by pitch and loudness. Pitch is related to the frequency of human voice, while loudness is related to the magnitude of human voice. Someone who does not have vocal cords, has no ability to produce voice. This problem is suffered by laryngectomy patients. Over half of all laryngectomy patients worldwide use electrolarynx for the rehabilitation of their speech ability. Unfortunately, the electrolarynx voice produces monotonic and flat intonation. Small changes in pitch and loudness of electrolarynx will give a better expression in laryngectomy patients. However, previous researches have focused on utilization of electromyography (EMG) signal of neck muscle for only pitch control. In this research, the relationship between human voice intonation (i.e. frequency and magnitude) and EMG signals of neck muscles was studied by looking for their correlation and their mutual information. Human voice signal and EMG signal of neck muscle were recorded simultaneously while subjects were saying "A" with varying intonation. The EMG signal of neck muscle was processed using amplifying, filtering, rectifying and "moving average" process. On the other hand, the human voice was processed by FFT Algorithm to obtain magnitude and fundamental frequency. The result shows that the correlation coefficient between human voice magnitudes and EMG signal of neck muscle is 0.93, while the correlation coefficient between human voice frequency and EMG signal of neck muscle is 0.88. Moreover, the mutual information between human voice magnitudes and EMG signal of neck muscle is 1.07, while the mutual information between human voice frequency and EMG signal of neck muscle is 0.65. These results show that the relationship between human voice magnitudes and EMG signal of neck muscle is stronger than the relationship between human voice frequencies and EMG signal of neck muscle. Therefore, it is more appropriate to use the EMG signal of neck muscle for controlling loudness of electrolarynx than that of the pitch of electrolarynx.

In medical diagnosis based on intuitionistic fuzzy sets (IFS), it is a general method to use max–min composition based on relations between the symptoms and diseases. The method, however, has been known to lead to quite conservative results and to a loss of information because the composition neglects most values except for extreme ones. To complement the shortcomings of the max–min composition, other measurements such as similarity and distance between IFS have gained attention as an important content in fuzzy mathematics from researchers. However, the methods based on the similarity and distance also have some drawbacks in that they provide an unclear diagnosis. For example, the distances for each disease do not have generally the same distribution. Therefore, there is a difference between the distance 0.25 for disease A and that for disease B. In addition, the difference in the distances for each disease would be very small. It can therefore be inferred that there would be no definite diagnostic criteria. To solve the problems, we propose a new approach for medical diagnosis based on Hamming distances in this study. In the approach, we do not use directly the Hamming distances but the distributional characteristics of the distances such as quantiles and p-values as a measure for diagnosis. To explore the potential for utilization of the approach, we present the simulation results. The simulation was applied to differentiate patients according to the three main types of primary headaches: migraine, tension and cluster headache. The result of the simulation indicates that it is possible to classify headache using the proposed method.

An efficient detection of microcalcification based on edge enhancement using discrete wavelet transform (DWT) is presented in this paper. The proposed method is implemented by separating the wavelet coefficients into weak and strong edge coefficients for effective detection of microcalcification. Identification of strong and weak edge locations corresponding to microcalcification is obtained by allowing the input image through appropriate filters before wavelet decomposition. Before reconstructing the output image, the strong and weak edge coefficients are modified based on the energy of the coefficients. The reconstructed image exhibits a better enhancement with the fine detail components of microcalcification than the original mammogram image. Standard Mias mammogram database images and clinical mammograms are used for testing and comparing subjective and objective measures of the mammogram images. A comparative study is made with the existing state-of-the-art edge enhancement and contrast enhancement methods and results are encouraging. The edge emphasizing ability of the proposed method is highly proficient in detection of microcalcification from the mammogram.

Arterio-Venous (AV) grafts which are used for hemodialysis, frequently develop intimal hyperplasia (IH) in venous anastomosis and ultimately leads to graft failure which is the major cause of morbidity and mortality in patients with the end stage renal disease (ESRD). Although the high AV access blood flow has been implicated in the pathogenesis of graft stenosis, the potential role of needle turbulence during hemodialysis is relatively unexplored. In the current study, a numerical survey is carried out to investigate the effect of venous needle's flow on hemodynamic parameters of the flow inside a hemodialysis graft. Two cases include the individual graft flow and the graft flow combined with a needle flow which both of them are evaluated and their results are compared to the cell morphology are represented in this research. Blood is considered as an incompressible Newtonian fluid with density of 1050 [Kg/(m³)] and dynamic viscosity of 0.0035 [Pa.s] and the graft was considered as a rigid vessel due to its low compliance. Flow rates were considered 500 and 100 [mL/min] as inlet conditions of the graft and the needle, respectively. Results show that the presence of the needle's flow causes to increase the amount of shear stress and nonuniformity of the shear stress distribution on the graft wall, where the cell alignment is lost in the presence of needle flow. These findings suggest the presence of potential relationship between venous needle flow during hemodialysis and endothelial dysfunction which may lead to vascular injury and graft failure.

Expectation–maximization (EM) algorithm has been extensively applied in brain MR image segmentation. However, the conventional EM method usually leads to severe misclassifications MR images with bias field, due to the significant intensity inhomogeneity. It limits the applications of the conventional EM method in MR image segmentation. In this paper, we proposed an interleaved EM method to perform tissue segmentation and bias field estimation. In the proposed method, the tissue segmentation is performed by the modified EM classification, and the bias field estimation is accomplished by an energy minimization. Moreover, the tissue segmentation and bias field estimation are performed in an interleaved process, and the two processes potentially benefit from each other during the iteration. A salient advantage of the proposed method is that it overcomes the misclassifications from the conventional EM classification for the MR images with bias field. Furthermore, the modified EM algorithm performs the soft segmentation in our method, which is more suitable for MR images than the hard segmentation achieved in Li et al.'s¹² method. We have tested our method in the synthetic images with different levels of bias field and different noise, and compared with two baseline methods. Experimental results have demonstrated the effectiveness and advantages of the proposed algorithm.

Unintentional falls cause serious health problem and high medical cost, particularly among the elders. Efficient fall detection can ensure fallen subjects with timely rescue, less pain and lower health-care expense. However, the accuracy of the present fall detection system with single accelerometer does not meet the requirement of practical application. In this paper, a fall detection method using three wearable triaxial accelerometers and a decision-tree classifier is proposed. The three triaxial accelerometers are, respectively mounted on the head, the waist and the ankle to capture the acceleration signals of human movement. A Kalman filter is adopted to estimate the body tilt angle. After the features are extracted, the trained decision-tree model is used to predict the fall. The efficiency improvement is evidenced by the scripted and unscripted lateral fall experiments, involving five young healthy volunteers (three males and two females; age: 23.3 ± 1 years). The classification of fall and activities of daily living (ADL) achieve recall, precision and F-value of 93.1%, 95.9%, and 94.5%, respectively, and the system detects all falls during the extended unscripted trials. The experimental results indicate that the complementary movement information coming from three accelerometers can enhance the performance of fall detection. The proposed method is efficient, and it has remarkable improvements in comparison to the method of using one or two accelerometers.

The case-based reasoning (CBR) approach consists of retrieving solutions from similar past problems and adapting them to new problems. Interpolation tools can easily be used as adaptation tools in CBR systems. The accuracies of interpolated results depend on the set of known solved problems with which the interpolation tools are previously trained. EquiVox is a CBR-based system designed for retrieving and adapting three-dimensional numerical representations of human organs called phantoms. EquiVox uses an interpolation tool as an adaptation process. These phantoms are used by radiation protection experts to establish dosimetric reports in case of accidental overexposure to radiation. In addition, medical physicians need these phantoms to compute and control exposure to radiation used to treat diseases such as cancerous tumors in hospitals. The present work aims at proposing a distributed architecture for EquiVox so that a user may find a solution as quickly as possible based on the most recent available knowledge of a given community. We have designed a distributed architecture based on a multiagent paradigm and studied the theoretical performance of the new version. The ability of the new system to quickly provide and adapt solutions using the most up-to-date knowledge has been analyzed from a probabilistic angle. In the case of limited and accidental exposure to radiation, the proposed parallel processing system improves the previous and sequential version of EquiVox. Improvements are also obtained in some cases of massive exposure to radiation.

Collagen and noncollagenous proteins have an important role in the formation of mineral constituent of bone matrix. In this research, the morphology and phase characteristics of calcium phosphate nanoparticles in presence of collagen were investigated. The synthesis reaction was initiated by mixing H₃PO₄ as phosphorous source and CaCl₂ as calcium source and type I collagen. Collagen concentration in suspension and Ca to P ratio was 1% and 1.67, respectively. The samples (with collagen and without collagen), were heat treated at 600°C and characterized by X-Ray diffraction (XRD), Fourier transformation infrared (FTIR) and scanning electron microscopy (SEM). More smaller and flake-like shape particles were observed in the SEM images of sample synthesized in the presence of collagen compared to the control sample which was constituted of larger granular particles. The XRD results revealed that the synthesized mineral powders with collagen were composed of hydroxyapatite and octacalcium phosphate. P–O and OH characteristic peaks were identified in FTIR spectra. In hybrid sample, the shift of amides band, revealed the electrostatic interactions between calcium phosphate ions and carboxyl or amino groups of collagen fibrils. The Ca to P molar ratio for sample with collagen was 1.9. It was found that the sample synthesized in the presence of collagen has a similar microstructure to natural bone.

One of the general methods for particle separation in lab-on-a-chips (LOCs) is dielectrophoresis (DEP). The effects of electrode isolation in DEP-based particle separation devices are discussed throughout this paper. One advantage of the electrode isolation is reducing electrode–electrolyte–sample mutual interactions. In this study, the conventional DEP forces using interdigitated electrode arrays is numerically investigated without and in the presence dielectric layer in the interface of electrode and electrolytic fluid. The study includes the effect of dielectric layer thickness when fluids of different electrical conductivity are involved. The results show that the electric field and also intra-channel gradient of electric field square depend on frequency and an isolating layer acts as high-pass filter, thus frequency response of conventional DEP force besides Clausius–Mossotti (CM) factor is depended on the electric field gradient. The results also show that in contactless model, the frequency response of the DEP forces can be engineered by dielectric thickness and electrical conductivity of the suspending medium. According to the obtained results, during particle separation with DEP method in the presence of insolated electrodes and considering the reduction in electric field intensity, an appropriate and optimal choice for working frequency, voltage of electrodes and thickness of dielectric layer should be considered. The particles and dielectric isolated layer under study is polystyrene beads and polydimethylsiloxane (PDMS) elastomeric polymer, respectively.

Independent component analysis (ICA) has been proven to be a powerful tool for removing artifacts from electroencephalogram (EEG) recordings in the form of blind source separation (BSS). Independent components (ICs) come from undesired sources that are mixed with the useful signal, and the assessment of such ICs allows them to be detected. But the unwanted ICs also can contain some useful information. To overcome this problem, wavelet-enhanced ICA (wICA) can be used, and this method applies a wavelet threshold for each wavelet coefficient to suppress abnormal deformation in each wavelet coefficient. Using the wICA algorithm to suppress artifacts provides an EEG signal with less distortion in the amplitude and in the phase of the cerebral part of the EEG, and the cerebral part of the EEG can be estimated and obtained very similar to control conditions. However, the EEG signals are affected by various artifact components, and those that have the greatest influence are electromyography (EMG) and electrooculography (EOG). These artifacts may appear simultaneously, randomly or interruptedly, so a fixed threshold level is not really appropriate. We proposed a system including wICA integrated with an adaptive filter model, and this combination system can provide the best prediction of the impacts of artifacts to set up a threshold value that is adaptive and suitable. Our experimental results showed that are approach provided better rejection of artifacts than the wICA system.

Learning to control the planar three-link musculoskeletal arm by using an Actor–Critic learning algorithm during reaching movements to stationary target is presented. The arm model used in this study includes three skeletal links (hand, forearm and upper arm), three joints (wrist, elbow and shoulder without redundancy) and six nonlinear monoarticular muscles with redundancy which are modeled based on Hill model. The learning system is composed of Actor and Critic parts. For each part, a single layer neural network is used. This learning system applies six activation commands to six muscles at each instant of time. It also uses a reinforcement (reward) feedback for learning process and controlling the arm movement direction. The results showed that with a learning rate α = 0.9 and after 20 episodes, Mean square error (MSE), average reward and average time of reaching the target are gradually converged to the values: 0.0056, 0.02262 and 187 s, respectively. After the 20th episode, the learning will be completed. The research suggests a new direction for designation of learning-based controllers for functional electrical stimulation (FES) applications and for arm movement of autonomous robots.