The surface electromyographic (SEMG) signal is very convenient for prosthesis control because it is non-invasively acquired and intrinsically related to the user's intention. This work presents a feature extraction and pattern classification algorithm for estimation of the intended knee joint angle from SEMG signals acquired using two sets of electrodes placed on the upper leg. The proposed algorithm uses a combination of time-domain and frequency-domain approaches for feature extraction (signal amplitude histogram and auto-regressive coefficients, respectively), a self-organizing map for feature projection and a Levenberg-Marquardt multi-layer perceptron neural network for pattern classification. The new algorithm was quantitatively compared with the method proposed by Wang et al (2006 Med. Biol. Eng. Comput. 44 865-72), which uses wavelet packet feature extraction, principal component analysis and a multi-layer perceptron neural classifier. The proposed method provided lower error-to-signal percentage and peak error amplitudes, higher correlation and fewer error events. The algorithm presented in this work may be useful as part of a myoelectric controller for active leg prostheses designed for transfemoral amputees.

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This paper presents a hybrid adaptive algorithm for the compression of surface electromyographic (S-EMG) signals recorded during isometric and/or isotonic contractions. This technique is useful for minimizing data storage and transmission requirements for applications where multiple channels with high bandwidth data are digitized, such as telemedicine applications. The compression algorithm proposed in this work uses a discrete wavelet transform for spectral decomposition and an intelligent dynamic bit allocation scheme implemented by an approach using the Kohonen layer, which improves the bit allocation for sections of the S-EMG with different characteristics. Finally, data and overhead information are packed by entropy coding. The results for the compression of isometric EMG signals showed that this algorithm has a better performance than standard wavelet compression algorithms presented in the literature (presenting a decrease of at least 5% in per cent residual difference (PRD) for the same compression ratio), and a performance that is comparable with the performance of algorithms based on an embedded zero-tree wavelet. For isotonic EMG signals, its performance is better than the performance of the algorithms based on embedded zero-tree wavelets (presenting a decrease in PRD of about 3.6% for the same compression ratios, in the useful compression range).

This work presents a study on the influence of the aqueous environment on the surface EMG (sEMG) signal recorded in bipolar montage from the abductor pollicis brevis muscle, when only the forearm is immersed in water. Ten men, 30.1+/-4.0 (mean +/- SD) years old, performed ten 2-s 40% MVC isometric contractions of the abductor pollicis brevis muscle in two controlled environments (air and water, at a temperature of 32 degrees C). They were always equipped with electrodes protected with a waterproof adhesive tape. No significant variations (paired Wilcoxon test) due to the environments were observed in the median frequency of the power spectrum (MDF) and in the root mean square (RMS) value of the sEMG signal. These results allow us to assess the methodological criteria to properly record sEMG signals in water and provide the basis to explain different findings obtained by other authors.

The main limitation of radiofrequency (RF) ablation numerical simulations reported in the literature is their failure to provide statistical results based on the statistical variability of tissue thermalelectrical parameters. This work developed an efficient probabilistic approach to hepatic RF ablation in order to statistically evaluate the effect of four thermal-electrical properties of liver tissue on the uncertainty of the ablation zone dimensions: thermal conductivity, specific heat, blood perfusion and electrical conductivity. A deterministic thermal-electrical finite element model of a monopolar electrode inserted in the liver was coupled with the unscented transform method in order to obtain coagulation zone confidence intervals, probability and cumulative density functions. The coagulation zone volume, diameter and length were 10.96 cm 3 , 2.17 cm and 4.08 cm, respectively (P < 0.01). Furthermore, a probabilistic sensitivity analysis showed that perfusion and thermal conductivity account for >95% of the variability in coagulation zone volume, diameter and length.