Techniques of EMG signal analysis: detection, processing, classification and applications

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications

Wireless Ultrasound Video Transmission for Stroke Risk Assessment: Quality Metrics and System Design

In this paper we discuss the use of clinical quality criteria in the assessment and design of ultrasound video compression systems. Our goal is to design efficient systems that can be used to transmit quality ultrasound videos at the lowest possible bitrates. This led us to the development of a spatially- varying encoding scheme, where quantization levels are spatially varying as a function of the diagnostic significance of the video. Diagnostic Regions of Interest (ROIs) for carotid ultrasound medical video are defined, which are then used as input for Flexible Macroblock Ordering (FMO) slice encoding. Diagnostically relevant FMO slice encoding is attained by enabling variable quality slice encoding, tightly coupled by each region's diagnostic importance. Redundant Slices (RS) utilization increases compressed video's resilience over error prone transmission mediums. We present preliminary findings on three carotid ultrasound videos at CIF resolution, for packet loss rates up to 30%. Subjective quality evaluation incorporates a clinical rating system that provides for independent evaluations of the different parts of the video. Experimental results show that encoded videos attain enhanced diagnostic performance under noisy environments, while at the same time achieving significant bandwidth requirements reductions

Chemistry Central Journal Poster presentation Multi-objective de novo drug design using evolutionary graphs

© 2008 Nicolaou and Pattichis Drug discovery and development is a complex, lengthy process and failure of a candidate molecule can occur as a result of a combination of reasons, such as poor pharmacokinetics, lack of efficacy or toxicity. Drugs compromise the numerous, sometimes competing objectives so that the benefits to patients outweigh potential drawbacks and risks [1]. De novo drug design, involves searching an immense space of feasible, drug-like molecules to select those with the highest chances of becoming drugs using computational technology [2]. Traditionally, de novo design has focused on designing molecules satisfying a single objective, such as a similarity value to a known ligand or a virtual screening score, and ignored the presence of the multiple objectives required for drug-like behavior

The effect of color correction of endoscopy images for quantitative analysis in endometrium

The objective of this study was to develop a standardized protocol for the capturing and analysis of endoscopy digital images for subsequent use in a computer aided diagnosis (CAD) system in gynaecological cancer. Images were captured at optimum illumination and focus at 720times576 pixels using 24 bits color in the following cases: (i) for a variety of testing targets from a color palette with known color distribution, (ii) different viewing angles and distances from calf endometrium, and (iii) images from the human endometrium. Images were then gamma corrected and their classification performance was compared against that of non-gamma corrected images. No significant difference in texture features was found between the close up and panoramic views, and between angles, either before or after gamma correction. There was significant difference in certain texture features between normal and abnormal endometrium, both before and after gamma correction. Our findings suggest that proper color correction can significantly impact CAD system performance, and we recommend its application prior to quantitative texture analysis in gynaecological endoscop

Color Based Texture - Classification of Hysteroscopy Images of the Endometrium

The objective of this study was to develop a CAD system for the classification of hysteroscopy images of the endometrium based on color texture analysis for the early detection of gynaecological cancer. A total of 416 Regions of Interest (ROIs) of the endometrium were extracted (208 normal and 208 abnormal) from 40 subjects. RGB images were gamma corrected and were converted to the HSV and YCrCb color systems. The following texture features were extracted for each channel of the RGB, HSV, and YCrCb systems: (i) statistical features, (ii) spatial gray level dependence matrices and (iii) gray level difference statistics. The PNN statistical learning and SVM neural network classifiers were also investigated for classifying normal and abnormal ROIs. Results show that there is significant difference (using the Wilcoxon Rank Sum Test at a=0.05) between the texture features of normal and abnormal ROIs of the endometrium. Abnormal ROIs had higher gray scale median, variance, entropy and contrast and lower gray scale median and homogeneity values when compared to the normal ROIs. The highest percentage of correct classifications score was 79 % and was achieved for the SVM models trained with the SF and GLDS features for differentiating between normal and abnormal ROIs. Concluding, a CAD system based on texture analysis and SVM models can be used to classify normal and abnormal endometrium tissue. Further work is needed to validate the system in more cases and organs

Chemistry Central Journal Poster presentation Knowledge-driven multi-objective de novo drug design

© 2009 Nicolaou et al; licensee BioMed Central Ltd. Drug discovery is an inherently multi-objective process since drugs need to satisfy not only activity requirements but also a range of other properties such as selectivity and toxicity. However, drug discovery process practices, including both experimental and computational methods, commonly ignore this fact and focus on a single pharmaceutical objective at a time. De novo design, the branch of chemoinformatics addressing the in silico design of ligands from scratch, follows a similar approach typically focusing on a single objective, such as an interaction score to a target receptor or similarity to a known drug [1]. Recently, methods have appeared in the literature that attempt to design molecules satisfying multiple predefined objectives [2]. Motivated from the initial success o