Biomedical Engineering Laboratory
Biomedical Engineering Laboratory
Li M.-Z.,Biomedical Engineering Laboratory |
Ieong C.-I.,Biomedical Engineering Laboratory |
Law M.-K.,University of Macau |
Mak P.-I.,University of Macau |
And 2 more authors.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS | Year: 2013
Portable/Implantable biomedical applications usually exhibit stringent power budgets for prolonging battery life time, but loose operating frequency requirements due to small bio-signal bandwidths, typically below a few kHz. The use of sub-threshold digital circuits is ideal in such scenario to achieve optimized power/speed tradeoffs. This paper discusses the design of a sub-threshold standard cell library using a standard 0.18-μm CMOS technology. A complete library of 56 standard cells is designed and the methodology is ensured through schematic design, transistor width scaling and layout design, as well as timing, power and functionality characterization. Performance comparison between our sub-threshold standard cell library and a commercial standard cell library using a 5-stage ring oscillator and an ECG designated FIR filter is performed. Simulation results show that our library achieves a total power saving of 95.62% and a leakage power reduction of 97.54% when compared with the same design implemented by the commercial standard cell library (SCL). © 2013 IEEE.
Kutsuna T.,Tokai University |
Sato M.,Tokai University |
Ishihara M.,National Defense Medical College |
Furukawa K.S.,Biomedical Engineering Laboratory |
And 7 more authors.
Tissue Engineering - Part C: Methods | Year: 2010
Regenerative medicine requires noninvasive evaluation. Our objective is to investigate the application of time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) using a nano-second-pulsed laser for evaluation of tissue-engineered cartilage (TEC). To prepare scaffold-free TEC, articular chondrocytes from 4-week-old Japanese white rabbits were harvested, and were inoculated at a high density in a mold. Cells were cultured for 5 weeks by rotating culture (RC) or static culture (SC). The RC group and SC group at each week (n=5), as well as normal articular cartilage and purified collagen type II (as controls), were analyzed by TR-LIFS. The peak wavelength was compared with those of type II collagen immunostaining and type II collagen quantification by enzyme-linked immunosorbent assay and tensile testing. The fluorescence peak wavelength of the TEC analyzed by this method shifted significantly in the RC group at 3 weeks, and in the SC group at 5 weeks (p<0.01). These results correlated with changes in type II collagen (enzyme-linked immunosorbent assay) and changes in Young's modulus on tensile testing. The results were also supported by immunohistologic findings (type II collagen staining). Our findings show that TR-LIFS is useful for evaluating TEC. © 2010 Mary Ann Liebert, Inc.
Messadi M.,Biomedical Engineering Laboratory |
Bessaid A.,Biomedical Engineering Laboratory |
Taleb-Ahmed A.,French National Center for Scientific Research
Journal of Mechanics in Medicine and Biology | Year: 2010
Our objective in this paper is to introduce the efficacies of texture in the interpretation of color skin images. Melanoma is the most malignant skin tumor, growing in melanocytes, the cells responsible for pigmentation. This type of cancer is nowadays increasing rapidly; its related mortality rate increases by more modest and inversely proportional to the thickness of the tumor. This rate can be decreased by an earlier detection and better prevention. Using the features of skin tumors, such as color, symmetry, and border regularity, an attempt is made to determinate if the skin tumor is a melanoma or a benign tumor. In this work, we are interested by adding to form parameters such as the asymmetry (A) and the shape irregularities of skin tumors (B), the textural parameters to estimate colors in dermatoscopic images. In this case, the images are analyzed using textural parameters computed in several directions. These parameters and the form parameters are added to obtain a better classification results. A statistical analysis is performed over these ratios to select the most highly discriminating textural parameters. The method has been tested successfully on 144 images and we found significant differences between the lesions (melanoma and benign). Finally, these parameters (form and parameters of texture selected) are only use to classify the benign and malignancy of the skin lesion. A multilayer neural network is employed to differentiate between malignant tumors and benign lesions. © 2010 World Scientific Publishing Company.
Andrade A.O.,Biomedical Engineering Laboratory |
Pereira A.A.,Biomedical Engineering Laboratory |
Jr C.G.P.,Biomedical Engineering Laboratory |
Kyberd P.J.,University of New Brunswick
Biomedical Signal Processing and Control | Year: 2013
This work introduces a novel human-computer interface based on electromyography (EMG). This tool allows the user to control the cursor on a computer screen through EMG activity resulting from specific facial movements. This type of human-computer interface may be useful for individuals who want to interact with computers and suffer from movement limitations of arms and hands. Although there are a number of EMG-based human-computer interfaces described in literature, most of them are not assessed with regard to the learning curve resulting from the interaction with such interfaces, being this factor one of the main contributions of the presented study. Another contribution of the investigation is the proposal and evaluation of a complete and practical solution that implements a two-channel EMG interface for generating seven distinct states which can be used as output commands. In the study, a Finite State Machine, which is the core of the system, is responsible for the conversion of features extracted from EMG signals into commands (i.e., SINGLE-CLICK, UP, DOWN, LEFT, RIGHT, ROTATE, and ON-STANDBY) used for the control of the cursor on a computer screen. The tool uses only two channels of information that combines the muscle activity of three facial muscles, i.e., the Left and Right Temporalis and the Frontalis. In order to evaluate learning when using the tool a customized graphical user interface was devised. This interface allowed subjects to execute pre-defined timed actions with distinct levels of difficulty. In total, 10 healthy subjects and a single subject suffering from muscular dystrophy were involved in the experiments. Approximately 60 h of practical experiments were carried out. The results suggest that just after one training session subjects could control the cursor on a computer screen, and also that incremental learning is verified over training sessions. Therefore, the devised tool may be integrated with specific programs and used by individuals whose facial muscles are not severely damaged. © 2012 Elsevier Ltd.
Behadada O.,Biomedical Engineering Laboratory |
Trovati M.,University of Derby
Proceedings - 2015 International Conference on Intelligent Networking and Collaborative Systems, IEEE INCoS 2015 | Year: 2015
In this paper, we discuss a method to define semi-automatically fuzzy partition rules to provide a powerful and accurate insight into cardiac arrhythmia. As suggested by our evaluation, this provide a robust, scalable, and accurate system, which can successfully tackle the challenges posed by the utilization of big data in the medical sector. © 2015 IEEE.
Lazouni M.E.A.,Biomedical Engineering Laboratory |
Daho M.E.H.,Biomedical Engineering Laboratory |
Settouti N.,Biomedical Engineering Laboratory |
Chikh M.A.,Biomedical Engineering Laboratory |
Mahmoudi S.,University of Mons
Studies in Computational Intelligence | Year: 2013
The application of machine learning tools has shown its advantages in medical aided decision. This paper presents the implementation of three supervised learning algorithms: the C4.5 decision tree classifier, the Support Vector Machines (SVM) and the Multilayer PerceptronMLP's in MATLAB environment, on the preoperative assessment database. The classification models were trained using a new database collected from 898 patients, each of whom being represented by 17 features and included in one among 4 classes. The patients in this database were selected from different private clinics and hospitals of western Algeria. In this paper, the proposed system is devoted to the automatic detection of some typical features corresponding to the American Society of Anesthesiolo-gists sores (ASA scores). These characteristics are widely used by all Doctors Specialized in Anesthesia (DSA's) in pre-anesthesia examinations. Moreover, the robustness of our system was evaluated using a 10-fold cross-validation method and the results of the three proposed classifiers were compared. © Springer International Publishing Switzerland 2013.