Biomedical Engineering: Applications, Basis and Communications

Cervical cancer cell images play an important part in diagnosing the cancer among the females worldwide. Existing noises, overlapping cells, mucus, blood and air artifacts in cervical cancer cell images makes their classification a hard task. It makes it difficult for both pathologists and intelligent systems to segment and classify them into normal, pre-cancerous and cancerous cells. However, true cell segmentation is needed for pathologists to make for accurate diagnosis. In this paper, a review of algorithms used for cervical cancer cell image classification is presented. This includes pre-processing steps (noise reduction and cell segmentation/without segmentation), feature extraction, and intelligent diagnosis systems and their evaluations. Finally, future research trends on cervical cell classification to achieve complete accuracy are described.

Manometry using fiber Bragg gratings as sensing elements was recently developed and shows promising results. The use of fiber gratings in manometry has many advantages over using solid-state or water-perfused techniques. One of these is noise immunity, and adding more sensors does not require scaling the catheter diameter. However, temperature compensation is a required design criterion in fiber-based manometry. One solution is to use a catheter with two fibers: one for pressure measurements and the other for temperature compensation. This paper presents the steady-state and transient effects of temperature changes in fiber-based manometry. In vitro measurements of the catheter’s temperature-related characteristics are presented and discussed. This paper also introduces the use of a fitting function to compensate for the transient temperature response of the catheter.

The main aim of this paper is a contribution to the design of an Intelligent Diagnosis System for Cardiac anomalies detection in acquired ECG (Electrocardiogram) signals. To attain this goal, the authors have developed two approaches to extract the most relevant and significant parameters that can best classify ECGs into two classes namely, Normal and Abnormal from real Electrocardiogram signals. The first approach is a time method which consists in modeling ECGs using RBF (Radial Basis Function) neural network algorithm, whereas, the second approach is a frequency based method which relies on the DWT (Discrete Wavelet Transform) that projects the signal into a time-scale (time-frequency) plane. In both approaches, the resulting data are submitted to the same SVM (Support Machine Vector) Classifier. The results obtained have shown a good adjustment of relevant parameters for each approach and have revealed the most efficient combined processing-classification algorithm that has achieved classification rates up to 100 % allowing at a time a reduced number of parameters. This fact had considerably simplified the hardware for a real time implementation. Finally, the authors compared both approaches and have identified some implementation constraints, such as the sampling frequency for the frequency based approach and preprocessing and cutting for the time approach.

Powered prosthetic feet (PPF) are designed to provide transtibial amputees (TTA) with active propulsion and range of motion similar to that of the biological limb. Previous studies have demonstrated the PPF’s ability to increase TTA walking speeds while reducing the energetic costs, however, little is known about its effects on dynamic balance control. The purpose of this pilot study was to assess dynamic balance control in TTA subjects during level ground walking and obstacle-crossing tasks. Control subjects ($n=6$) and TTA subjects ($n=4$) were instructed to complete a series of functional walking tasks. The TTA subjects completed the walking protocol twice, first in their passive energy-storing prosthetic foot (ESPF) and again in the prescribed PPF after two weeks of acclimation. Motion data were collected via a 10-camera system with a 53-marker and 15-segment body model. Whole body medial-lateral center of mass motion (displacement and peak velocity) was analyzed and used as a functional indicator of dynamic balance control. Findings indicate no difference in the dynamic balance control of TTA wearing the PPF compared to the ESPF. However, there was an observed trend of walking speed and obstacle height affecting balance control within the groups.

Based on the data from the spiral Computerized Tomography (CT) images, three dimensional models consisting of the airway from nasal cavity, pharynx and trachea to triple bifurcation are built. Computational fluid dynamics (CFD) method is applied to study the respiratory air flow dynamics within the upper respiratory tract of human body. The influence of different respiratory routes on airway resistance and within the respiratory air flow characteristics is studied under the respiration of the nose and mouth. The influence of the different respiratory route on the characteristics of airflow and the airway resistance were studied at the work of nasal and oral. When only a small amount of air flow is inhaled and exhaled through the oral, the distribution law of airflow in the airways is similar to that of breathing process only through the nose. With the increase of mouth breathing air flow rate, the airway of the highest airflow velocity and the total pressure drop are reduced. Except for the nasal cavity, where the airway resistance was decreased, the airway resistance of other parts was increased with increasing mouth breathing. When the airflow exchanged with the outside world mainly through mouth, the distribution of airflow in the airways were obviously different, especially in the nose, mouth and throat, which brought the new allocation of the airway resistance and the great reduction of nasal resistance. The airway resistance mainly concentrated in the oral cavity and the following airway. The results of numerical simulation are useful for studying the pathogenesis and diagnosing the diseases related to the anatomical structure and function of upper airway.

Polymeric composite materials hold promise for versatile advanced applications. Of utmost importance for these applications is incorporating inorganic particles within polymer matrices which lead to multifunctional polymeric composites with desired functions. Specifically, thermosensitive polymeric hydrogels incorporating particle fillers have elicited widespread interest because of promising applications in drug delivery, tissue engineering, and medical devices. Although these materials are frequently discussed in many research fields, there are no decisive conclusions reported in literature, showing how the particle filler affects the rheological and mechanical behaviors of the resulting hydrogels. In this research, hydroxyapatite (HAp) bioceramics with definable morphologies were synthesized in order to reveal their effects on the resulting properties of HAp/polymer composite hydrogels. HAp particles with spherical, sheet-like and rod-like shapes were prepared with assistance by adding amphiphilic surfactant, poly(ethylene oxide)-b-poly(propylene oxide)-b-poly(ethylene oxide) in synthesis. Thermosensitive composite hydrogels with controllable rheological and mechanical properties were thus developed by incorporating HAp particles into poly(ethylene glycol)-b-poly(lactide-co-glycolide) (PEG-PLGA) hydrogel. Experimental results revealed that the rheological and mechanical properties of the resultant HAp/PEG-PLGA composite hydrogel not only influenced by the added HAp particle amount, but also by the particle morphology and interactions between particles and hydrogels. The findings from this research provide a critical guideline for designing thermosensitive composite hydrogels with required rheological and mechanical properties.

Since a long term patency of the dental implant has a direct relationship with their biomechanical performance, it is of vital important to understand the stresses and deformations that happen during chewing around the dental implant and bone. However, this model so far has not been well realized and this is why in this study we aim to establish a Finite Element (FE) model to analyse the stresses and deformations. A trajectory approach has been used to implement the action of muscles into the mode. To do this, a cornflake bio is mounted between the teeth and force applied until the breakage of the food in mouth. Furthermore, an experimental study was performed using the Digital Image Correlation (DIC) method and a set of three markers used to verify the numerical observations. The results revealed that in the maxillary bones, the maximum stresses were located within the cortical bone surrounding the implant and within the neck of implant. In addition, as the elastic modulus of the food is increased the stress in cortical bone increased accordingly. The results also revealed that the highest stress in the system is 74% of the yield stress while this value has been reported as 41% in previous studies.

The current study evaluates the dynamics of the electroencephalogram signals during specific emotional states in order to obtain a detailed understanding of the affective EEG patterns. Employing recurrence analysis, the dynamical states of the emotional brain during visual stimuli is evaluated. Three channels of electroencephalogram time series (Fz, Cz, and Pz) available in eNTERFACE06_EMOBRAIN database are used in this study. Electroencephalogram signals are recorded from 5 subjects in three emotional categories: exciting negative (disgust), neutral and exciting positive (happy). Recurrence quantification analysis (RQA) was applied to study electroencephalogram morphological changes in different emotional states (happy, disgust, neutral). The ANOVA and $t$-test are done to detect significant differences in RQA measures of the EEGs. It has shown that the phase space trajectory becomes more periodic during exciting negative. In addition, the results reveal that in comparison with negative emotion and neutral, the behavior of EEGs in positive emotion is highly chaotic. Performing statistical analysis, significant differences were observed in recurrence rate, determinism, average diagonal, line length, Shannon entropy, laminarity, trapping time among three emotional evoked groups. Although these changes occurred in all EEG channels, a better distinction between each emotional state can be observed in Pz location. It seems that recurrence analysis is a promising non-linear approach for detecting instantaneous changes in the emotional EEG induced by visual stimuli. RQA has the potential to discover differences of signal features in response to an emotional stimulus.