Bioautomation

Volume 21, Issue 1
Biomedical systems
Locally-adaptive Myriad Filters for Processing ECG Signals in Real Time	5-18
Nataliya Tulyakova [ +/- abstract ][ full text ]
The locally adaptive myriad filters to suppress noise in electrocardiographic (ECG) signals in almost in real time are proposed. Statistical estimates of efficiency according to integral values of such criteria as mean square error (MSE) and signal-to-noise ratio (SNR) for the test ECG signals sampled at 400 Hz embedded in additive Gaussian noise with different values of variance are obtained. Comparative analysis of adaptive filters is carried out. High efficiency of ECG filtering and high quality of signal preservation are demonstrated. It is shown that locally adaptive myriad filters provide higher degree of suppressing additive Gaussian noise with possibility of real time implementation.
Classification and Clustering of Parkinson's and Healthy Control Gait Dynamics Using LDA and K-means	19-30
Akash Kumar Bhoi [ +/- abstract ][ full text ]
Problem arises when distinct morphologic changes are not seen in the electromyographic waveform of normal (control) and Parkinson's subjects during data interpretation. This study aimed to ascertain whether neuro-degenerative disease, e.g., Parkinson's disease (PD) affects gait and mobility with comparison to the healthy control. Fifteen subjects (left and right foot) from both the groups are selected where the signal is obtained using force-sensitive resistors (Gait Dynamics in Neuro-Degenerative Disease Data Base). The proposed methodology is divided into five parts: (i) 1 hr recording of gait dynamics data are segmented into three intervals (0-20 min, 20-40 min and 40-60 min); (ii) Normalization of each segmented data (20 min), i.e., preprocessing (noise and baseline drift removal); (iii) Then the frequency domain powers for each segments are calculated which further introduced features in the successive stages for classification and clustering; (iv) The classification of Parkinson's disease and healthy control group is accomplished using Linear Discriminant Analysis (LDA); (v) Clustering of these two classes is performed using K-means clustering algorithm taking same sets of features. Certainly the classification and clustering results signify the classification probability using frequency domain power of gait dynamics/electromyogram signal. The re-substitution error of LDA during classification is found to be 21.11%. Moreover, significant and precise classification and clustering results are achieved between PD and control taking left-right foot frequency domain power as classification features.
Pneumatic Artificial Muscles Force Modelling and the Position and Stiffness Control on the Knee Joint of the Musculoskeletal Leg	31-42
Jingtao Lei, Jianmin Zhu [ +/- abstract ][ full text ]
Pneumatic artificial muscles (PAMs) have properties similar to biological muscle and are widely used in robotics as actuators. A musculoskeletal leg mechanism driven by PAMs is presented in this paper. The joint stiffness of the musculoskeletal bionic leg for jumping movement needs to be analysed. The synchronous control on the position and stiffness of the joint is important to improve the flexibility of leg. The accurate force model of PAM is the foundation to achieving better control and dynamic jumping performance. The experimental platform of PAM is conducted, and the static equal pressure experiments are performed to obtain the PAM force model. According to the testing data, parameter identification method is adopted to determine the force model of PAM. A simulation on the position and stiffness control of the knee joint is performed, and the simulation results show the effectiveness of the presented method.
A Hybrid Model of PSO Algorithm and Artificial Neural Network for Automatic Follicle Classification	43-58
O. R. Isah, A. D. Usman, A. M. S. Tekanyi [ +/- abstract ][ full text ]
Polycystic Ovarian Syndrome (PCOS) is one of the leading causes of infertility in the world, but is a preventable disease when detected early. Detection of follicles in ultrasound images of the ovary is required for the diagnosis of PCOS. The manual method of detecting follicles is time consuming, laborious, error-prone and inconvenient for patients. However, methods used by the existing automated systems often lead to a reduction in accuracy, sensitivity and specificity due to the irregular and jagged edges of the follicles. This research work aims at achieving an improved specificity, sensitivity and accuracy of the system. In this report, a new technique for the automatic detection of follicles is implemented. Lee filter was used to despeckle the ultrasound images. Multiple features were then extracted from the images. Further, twelve of these features were selected as optimal values by the Particle Swarm Optimization algorithm. Then, these features were fed as input to the Multilayer Perceptron Artificial Neural Network. Upon training and testing the network, 98.3% accuracy, 100% sensitivity and 96.8% specificity were achieved.
Application of Improved SVM Image Segmentation Algorithm in Computer Tomography Image Analysis	59-68
Xinhao Ji [ +/- abstract ][ full text ]
Medical imaging is becoming increasingly important in clinical diagnosis. Ultrasound imaging, computed tomography, magnetic resonance imaging (MRI) and other new medical imaging technology greatly broadens the imaging diagnostic methods. Animal Computer Tomography (CT) imaging, as an animal model, is of great significance to guide the experimental research of clinical diagnosis, and the treatment of pet disease also has a pioneering significance. Image segmentation, as the basis of medical image processing and analysis, has played a vital role in clinical diagnosis and treatment from doctors. In this paper, the existing segmentation algorithm is improved based on the characteristics of CT images of animals. In this paper, we use the global optimization of the genetic algorithm to improve the traditional support vector machine classification algorithm. At the same time, the kernel function of the support vector machine algorithm is improved to promote the segmentation results. The experiments show that the algorithm in this paper has a better segmentation effect in the processing of CT images of animals.
Review Article. Electrophysiological Methods for Study of Changes in Visual Analyzer in Patients with Diabetes Mellitus	69-102
Elena Mermeklieva, Mikhail Matveev [ +/- abstract ][ full text ]
The electrophysiological (EF) methods are objective methods for studying the visual analyzer function. These include electroretinography (ERG), electrooculography (EOG) and visual evoked potentials (VEPs). ERG and EOG are used for diagnosis and monitoring of a number of diseases of the retina. VEPs depend on the functional integrity of the entire optical path from the retina through the optic nerve, optic tract, the optical radiation to the visual cortex. The electrophysiological methods are widely used in studying the function of the visual analyzer in the ophthalmic and neurological practice, for objectively measuring the visual acuity and the visual field in non-cooperative patients, small children and in simulation. Diabetes mellitus (DM) is a group of metabolic diseases characterized by hyperglycemia. One of the late complications of DM is diabetic retinopathy (DR). It is one of the most serious complications of diabetes, often leading to blindness. Nowadays, DR includes retinal neurodegeneration and microvascular complications. By EF studies can evaluate the function of the retina in diabetic patients in an objective manner using ERG, that reflects the EF activity of the neurons in the retina and VEPs, which indicate the electrical conductivity across the optic tract to the visual cortex.
A Short Report. Large Solitary Distant Metastasis of Cutaneous Squamous Cell Carcinoma to Skin Graft Site with Complete Response Following Definitive Radiotherapy	103-108
Luiz Paulo Barros de Moraes, Ivan Burchett, Stephanie Nicholls, Elizabeth Paton, Emily Forward, Gerald B. Fogarty [ +/- abstract ][ full text ]
Metastases of cutaneous squamous cell carcinoma (cSCC) in surgical sites distant from the primary lesion is anecdotally regarded as common but seldom reported. Patients with this condition usually have surgical treatment of the metastasis. The presented case is a 77-year-old immunocompetent male. He had surgery for a scalp primary cSCC that was closed with a split thickness skin graft (SSG). He developed a four centimetre (cm) solitary symptomatic metastatic cSCC in the SSG donor site on the right thigh 3 months after graft harvesting. There was a complete response of this metastasis following definitive curative radiotherapy until death from further metastatic disease six months later. Radiotherapy can be an alternative to surgery for large cSCC metastasis.
Bioprocess systems
A Numerical Solution of Volterra's Population Growth Model Based on Hybrid Function	109-120
Saeid Jahangiri, Khosrow Maleknejad, Majid Tavassoli Kajani [ +/- abstract ][ full text ]
In this paper, a new numerical method for solving Volterra's population growth model is presented. Volterra's population growth model is a nonlinear integro-differential equation. In this method, by introducing the combination of fourth kind of Chebyshev polynomials and Block-pulse functions, approximate solution is presented. To do this, at first the interval of equation is divided into small sub-intervals, then approximate solution is obtained for each sub-interval. In each sub-interval, approximate solution is assumed based on introduced combination function with unknown coefficients. In order to calculate unknown coefficients, we imply collocation method with Gauss-Chebyshev points. Finally, the solution of equation is obtained as the sum of solutions at all sub-intervals. Also, it has been shown that upper bound error of approximate solution is O(m^-r/N^1/2). It means that by increasing m and N, error will decrease. At the end, the comparison of numerical results with some existing ones, shows high accuracy of this method.
Dynamics Monitoring of Fed-batch E. coli Fermentation	121-132
Anastasiya Zlatkova, Velislava Lyubenova [ +/- abstract ][ full text ]
A new method for on-line dynamics monitoring of three physiological states of fed-batch E. coli fermentation is proposed. The method is based on the acetate kinetics accepted as adaptive key parameter for recognition of current physiological state. Software sensors of main kinetic parameters are derived. They are connected in cascade scheme with inputs on-line measurements of acetate and glucose concentrations. In the outputs the on-line information of three biomass growth rates and biomass concentration is received. Tuning of the estimation algorithms is realized. The efficiency of the proposed method is investigated by simulations using a new biochemical process model. Discussion about the accuracy of obtained estimates and their relationship with the values of tuning parameters is done. The proposed method could be applied for control algorithms design for each physiological state.
Bioecological systems
Generalized Net Model of Mechanical Wastewater Pre-treatment	133-144
Vania Georgieva [ +/- abstract ][ full text ]
In the current paper, a Generalized Net (GN) model of mechanical treatment of wastewater in a wastewater plant is presented. The technological scheme comprises the main processes in this first stage of wastewater treatment. The proposed GN-model allows for more complex monitoring and managing the individual steps in the mechanical stage of the treatment process. Moreover, it can be helpful in tracing, analyzing and setting of some parameters related to the operation of the equipment.
Short-term Effect of Nitrogen Addition on Microbial and Root Respiration in an Alpine Spruce Ecosystem	145-159
Jian Wang, Genxu Wang, Zhaoyong Hu [ +/- abstract ][ full text ]
Soil respiration plays an important role in the carbon (C) flux of the global C cycle and is greatly affected by nitrogen (N) additions in the form of deposition or fertilization. The aim of this study was to investigate the response of total soil respiration (Rs), microbial respiration (Rm), and root respiration (Rr) to short-term N addition and the potential mechanisms of short-term N deposition influencing soil respiration in an alpine spruce ecosystem. Four N treatment levels (0, 50, 100, 150 kg N ha^-1 year^-1) were applied monthly in a Picea balfouriana (commonly known as "alpine spruce") plantation beginning in November 2013 and Rs, Rm, and Rr were measured from May to November 2014. The results show that simulated N depositions stimulate Rs, Rm, and Rr and the beneficial effects decreased along N gradients from hourly to seasonal scales. The seasonal temperature coefficients (Q₁₀) of Rs, Rm, and Rr ranged from 2.50 to 3.8, 2.99 to 4.63, and 1.86 to 2.96, while the diurnal Q₁₀ ranged from 1.71 to 2.04, 1.89 to 2.32, 1.42 to 1.75, and there was a similar trend with soil respiration along N gradients. In addition, Rr showed significant positive correlation with fine root biomass, and Rm was likely driven by soil enzyme related to the microbial C cycle in the growing season. Our results indicate that short-term N addition stimulated fine root biomass and soil enzymatic activity to bring about a potential increase in soil respiration rates under low-N addition, while the opposite occurred under high-N addition.

Sponsored by National Science Fund of Bulgaria, Grant No DNP 05-40/2016