On the selection of the cost function for gradient-based decomposition of surface electromyograms

Recently, gradient Convolution Kernel Compensation method was introduced for blind assessment of sparse pulse sequences (PS) out of their convolutive mixtures. This method employs multichannel recordings, is fully automatic and is minimally biased by assumptions about underlying mixing process. In the first step, the unknown mixing channels (convolution kernels) are compensated, whereas in the second step the gradient algorithm is used to blindly optimize the estimated PSs. This paper discusses the selection of the cost functions for aforementioned gradient-based optimization and provides analytical framework for their mutual comparison. Theoretical derivations are validated on both synthetic signals with random mixing matrices and experimental surface electromyograms from abductor pollicis brevis muscle. The analytical derivations agree very well with the results obtained from numerical simulations and establish theoretical guidelines for developing new gradient-based decomposition methods.

Building interactive virtual environments for simulated training in medicine using VRML and Java/JavaScript

Medicine is a difficult thing to learn. Experimenting with real patients should not be the only option; simulation deserves a special attention here. Virtual Reality Modelling Language (VRML) as a tool for building virtual objects and scenes has a good record of educational applications in medicine, especially for static and animated visualisations of body parts and organs. However, to create computer simulations resembling situations in real environments the required level of interactivity and dynamics is difficult to achieve. In the present paper we describe some approaches and techniques which we used to push the limits of the current VRML technology further toward dynamic 3D representation of virtual environments (VEs). Our demonstration is based on the implementation of a virtual baby model, whose vital signs can be controlled from an external Java application. The main contributions of this work are: (a) outline and evaluation of the three-level VRML/Java implementation of the dynamic virtual environment, (b) proposal for a modified VRML Timesensor node, which greatly improves the overall control of system performance, and (c) architecture of the prototype distributed virtual environment for training in neonatal resuscitation comprising the interactive virtual newborn, active bedside monitor for vital signs and full 3D representation of the surgery room.

Correlation-based decomposition of surface electromyograms at low contraction forces

The paper studies a surface electromyogram (SEMG) decomposition technique suitable for identification of complete motor unit (MU) firing patterns and their motor unit action potentials (MUAPs) during low-level isometric voluntary muscle contractions. The algorithm was based on a correlation matrix of measurements, assumed unsynchronised (uncorrelated) MU firings, exhibited a very low computational complexity and resolved the superimposition of MUAPs. A separation index was defined that identified the time instants of an MU's activation and was eventually used for reconstruction of a complete MU innervation pulse train. In contrast with other decomposition techniques, the proposed approach worked well also when the number of active MUs was slightly underestimated, if the MU firing patterns partly overlapped and if the measurements were noisy. The results on synthetic SEMG show 100% accuracy in the detection of innervation pulses down to a signal-to-noise ratio (SNR) of 10 dB, and 93+/-4.6% (mean+/-standard deviation) accuracy with 0 dB additive noise. In the case of real SEMG, recorded with an array of 61 electrodes from biceps brachii of five subjects at 10% maximum voluntary contraction, seven active MUs with a mean firing rate of 14.1 Hz were identified on average.

Measurement of perifollicular blood flow of the dominant preovulatory follicle using three-dimensional power Doppler

OBJECTIVE

To establish whether we might predict the outcome of unstimulated in-vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) cycles with quantitative indices of perifollicular blood flow assessed with three-dimensional (3D) reconstruction of power Doppler images.

METHODS

This prospective study included an analysis of 52 unstimulated cycles. Color and power Doppler ultrasound examinations of a single dominant preovulatory follicle were performed on the day of oocyte pick-up. With 3D reconstruction and processing, quantitative indices were obtained i.e. the percentage of volume showing a flow signal (VFS) inside a 5-mm capsule of perifollicular tissue and the percentage of VFS of each of the three largest vessels in this capsule. These indices as well as pulsed Doppler indices were compared between the groups of cycles with different outcomes using a one-way ANOVA test.

RESULTS

In nine cycles no oocyte was retrieved (Group A), in seven cycles no fertilization occurred (Group B) and in 30 cycles no implantation occurred (Group C). Six cycles resulted in pregnancy (Group D). There were no statistically significant differences in pulsed and power Doppler indices between these groups. However, the percentage of VFS in the capsule was higher than average in cycles with implantation (19.22 +/- 16.82 vs. 12.42 +/- 8.89, NS) and the percentage of VFS in the main vessel exhibited lower than average values in cycles with implantation (20.66 +/- 10.05 vs. 39.84 +/- 20.15), but only reached borderline statistical significance (F = 2.457, P = 0.074).

CONCLUSION

It can be hypothesized that the follicles containing oocytes able to produce a pregnancy have a distinctive and more uniform perifollicular vascular network.

Cellular automata and follicle recognition problem and possibilities of using cellular automata for image recognition purposes

Cellular automata are discrete dynamical systems whose behaviour is completely specified in terms of a local relation. Guided by a suitable recipe, they can simulate a whole hierarchy of structures and phenomena. While investigating the problem of follicle recognition in ultrasonic images of women's ovaries, we became increasingly interested in using cellular automata for this purpose. We were very successful, which encouraged us to further investigate the use of cellular automata for image recognition purposes in general. This paper presents the results of our research in this area, along with the details of how we solved the follicle recognition problem.

Automated computer-assisted detection of follicles in ultrasound images of ovary

Monitoring the follicles in women's ovaries is especially important in human reproduction. Today, the monitoring of follicles is done with human interaction. Such monitoring can be very demanding and inaccurate, and in most cases signifies additional burdens for the experts. In this paper, a new algorithm for automated computer-assisted detection of follicles in the ultrasound images of the ovary is proposed. It has a typical object recognition scheme (preprocessing, segmentation, and classification). The algorithm is based on the following idea: first, the ovary is estimated (coarsely) and then follicles are searched for. The methods used are known from literature (despeckle filter, Kirsch's operator, optimal thresholding, thinning, shape descriptions, classification), and the majority of our work was done experimenting with these methods and selecting the appropriate thresholds. The algorithm's computational complexity is of order of O(n2), which means about 6 min of processing time per an ultrasound image of dimensions of 768 x 576 pixels on HP 715 machines. It has been tested on a set of 20 real ultrasound images of the ovary. The recognition rate of follicles with these procedures was around 62%. The algorithm is not perfect, but it will be further modified and improved, as indicated in our conclusions.