Medical & Biological Engineering & Computing

An instrument has been developed that allows foetal breathing movements to be recorded with a laboratory resolution of about 0·06 mm. The new tracking scheme is described in addition to the complete instrument incorporating a number of these tracking units for multichannel movement monitoring. The multiple tracking scheme is implemented by adapting a commercial real-time array and building a custom or special instrument to process the signals. The instrument is capable of simultaneously tracking up to six separate echo complexes selected on an electronically-switched real-time B-scan image of the foetus. It therefore can provide any permutation of three channels of differential movement at the same time. Early clinical results obtained using the new instrument are presented.

Electromyography (EMG)-based control is the core of prostheses, orthoses, and other rehabilitation devices in recent research. Nonetheless, EMG is difficult to use as a control signal given the complex nature of the signal. To overcome this problem, the researchers employed a pattern recognition technique. EMG pattern recognition mainly involves four stages: signal detection, preprocessing feature extraction, dimensionality reduction, and classification. In particular, the success of any pattern recognition technique depends on the feature extraction stage. In this study, a modified time-domain features set and logarithmic transferred time-domain features (LTD) were evaluated and compared with other traditional time-domain features set (TTD). Three classifiers were employed to assess the two feature sets, namely linear discriminant analysis (LDA), k nearest neighborhood, and Naïve Bayes. Results indicated the superiority of the new time-domain feature set LTD, on conventional time-domain features TTD with the average classification accuracy of 97.23 %. In addition, the LDA classifier outperformed the other two classifiers considered in this study.

The paper reports the results of investigations comparing the relative in vitro responses of different signal processing algorithms commonly employed in laser Doppler flowmetry (LDF). A versatile laser Doppler system is described which enabled complex signal processing to be implemented relatively simply using digital analysis. The flexibility of the system allowed a variety of processing algorithms to be studied by simply characterising the algorithm of interest under software control using a personal computer. An in vitro physical model is also presented which was used to maintain reproducible fluid flows. Flows of particles were studied in a physical model using both a near-infra-red (NIR) diode and an He/Ne laser source. The results show that frequency-weighted algorithms are responsive to both particle velocity and concentration, whereas non-weighted algorithms respond to concentration only. The linearity of the velocity response is critically dependent on both the dimensions of the in vitro model and the frequency bandwidth of the signalprocessing algorithm.

Human behaviour is complex because of the multiplicity of influences from which it arises, so that it is obvious, ona priori grounds, that an empathetic or ‘relating’ approach cannot continue to sustain all of our investigations. A reorientation in thinking about behaviour and related psychological changes is necessary for psychiatry and for psychometric testing in order that the foundations of assessment in such areas are as secure and objective as possible. The assessment of behaviour is moving towards repeated measurements on a variety of parameters, which necessitate the development of automated testing and data collection to sustain the increasing demands in these areas. This paper describes an off-line system of data logging and presents preliminary results which show that it is capable of operating with minimal subjective contamination and allows repeated objective assessment on various aspects of behaviour.

Positron emission tomography (PET) is a non-invasive molecular imaging method for quantitative observation of physiological and biochemical changes in living organisms. The quality of the reconstructed PET image is limited by many different physical degradation factors. Various denoising methods including Gaussian filtering (GF) and non-local mean (NLM) filtering have been proposed to improve the image quality. However, image denoising usually blurs edges, of which high frequency components are filtered as noises. On the other hand, it is well-known that edges in a PET image are important to detection and recognition of a lesion. Denoising while preserving the edges of PET images remains an important yet challenging problem in PET image processing. In this paper, we propose a novel denoising method with good edge-preserving performance based on spectral graph wavelet transform (SGWT) for dynamic PET images denoising. We firstly generate a composite image from the entire time series, then perform SGWT on the PET images, and finally reconstruct the low graph frequency content to get the denoised dynamic PET images. Experimental results on simulation and in vivo data show that the proposed approach significantly outperforms the GF, NLM and graph filtering methods. Compared with deep learning-based method, the proposed method has the similar denoising performance, but it does not need lots of training data and has low computational complexity.