Adson Ferreira da Rocha

Probabilistic finite element analysis of radiofrequency liver ablation using the unscented transform.

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 thermal-electrical 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.

Effect of variable heat transfer coefficient on tissue temperature next to a large vessel during radiofrequency tumor ablation

BackgroundOne of the current shortcomings of radiofrequency (RF) tumor ablation is its limited performance in regions close to large blood vessels, resulting in high recurrence rates at these locations. Computer models have been used to determine tissue temperatures during tumor ablation procedures. To simulate large vessels, either constant wall temperature or constant convective heat transfer coefficient (h) have been assumed at the vessel surface to simulate convection. However, the actual distribution of the temperature on the vessel wall is non-uniform and time-varying, and this feature makes the convective coefficient variable.MethodsThis paper presents a realistic time-varying model in which h is a function of the temperature distribution at the vessel wall. The finite-element method (FEM) was employed in order to model RF hepatic ablation. Two geometrical configurations were investigated. The RF electrode was placed at distances of 1 and 5 mm from a large vessel (10 mm diameter).ResultsWhen the ablation procedure takes longer than 1–2 min, the attained coagulation zone obtained with both time-varying h and constant h does not differ significantly. However, for short duration ablation (5–10 s) and when the electrode is 1 mm away from the vessel, the use of constant h can lead to errors as high as 20% in the estimation of the coagulation zone.ConclusionFor tumor ablation procedures typically lasting at least 5 min, this study shows that modeling the heat sink effect of large vessels by applying constant h as a boundary condition will yield precise results while reducing computational complexity. However, for other thermal therapies with shorter treatment using a time-varying h may be necessary.

Compression of EMG signals with wavelet transform and artificial neural networks

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).

Left ventricle segmentation in echocardiography using a radial-search-based image processing algorithm

A new left ventricle segmentation method in two-dimensional echocardiography images is proposed. Image processing techniques combined with radial search and temporal information are used to extract the left ventricle boundary. Borders from sequential images are extracted using the proposed method, and a curve illustrating the area variation within a cardiac cycle is presented. Performance evaluation is performed by comparing the borders obtained from the presented method to those manually prescribed by a medical specialist. The new sequential radial search algorithm improved the border extraction from long-axis ultrasound images, specially the ones where the mitral valve was open. Segmentation errors due to low contrast were corrected.

A tool for time-frequency analysis of heart rate variability

The analysis of heart rate variability (HRV) signals is an important tool for studying the autonomic nervous system, as it allows the evaluation of the balance between the sympathetic and parasympathetic influences on heart rhythm. Time-frequency analysis of HRV makes it easier to evaluate how this balance varies with time. This work presents a tool for time-frequency analysis of heart rate variability (HRV) developed in Matlab 6.5. Three techniques are available: Short-Time Fourier Transform, Continuous Wavelet Transform Scalogram and Time-Variant Auto-Regressive Modeling.

Estimation of the knee joint angle from surface electromyographic signals for active control of leg prostheses

The surface electromyographic (SEMG) signal is very convenient for prosthesis control because it is non-invasively acquired and intrinsically related to the users 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.

Semi-automatic detection of the left ventricular border

Two semi-automatic methods for the detection of the left ventricular border in two-dimensional short axis echocardiographic images are presented and compared. In these methods, the left ventricular area variation curve is calculated during a complete cardiac cycle after the segmentation of several frames. This allows the evaluation of the cardiovascular dynamics and the identification of important clinical parameters. The algorithms are proposed as several independent modules. The results are validated through the comparison between the semi-automatic continuous boundaries and manuals boundaries sketched by a medical specialist.

Heart rate, oxygen saturation, and skin conductance: A comparison study of acute pain in Brazilian newborns

Heart rate variability (HRV), oxygen saturation variability (OSV) and skin conductance activity (SCA) are recognized physiological markers of acute pain. In order to verify which of them has the best correlation with psychophysical parameters of pain (intensity, reactivity, direction, regulation and slope), an observational prospective study was performed, including 41 healthy full term newborns. The measurements studied were the HRV, the OSV, and the following SCA variables: number of waves per second (NWps) and relative area under the curve of waves (AUC). The measurements were performed in periods labeled before, during, and after a heel prick. The variation measured for intensity between periods was significant for the NWps (p=0.001), AUC (p=0.03), HRV (p=0.001) and OSV (p=0.004). Also, the reactivity and direction were significant for all variables, except AUC. The regulation parameter was significant for the variables NWps (p<0.01), AUC (p<0.05), HRV (p<0.01) and OSV (p<0.01). The slope was statistically significant only for the OSV variable (p=0.000). We concluded that the responses of the SCA, HRV and OSV to painful events fit the psychophysical parameters of a physiological marker and serve as valuable measures for pain diagnostic working the use in accordance with the needs of the context.

Semi-automatic algorithm for construction of the left ventricular area variation curve over a complete cardiac cycle

BackgroundTwo-dimensional echocardiography (2D-echo) allows the evaluation of cardiac structures and their movements. A wide range of clinical diagnoses are based on the performance of the left ventricle. The evaluation of myocardial function is typically performed by manual segmentation of the ventricular cavity in a series of dynamic images. This process is laborious and operator dependent. The automatic segmentation of the left ventricle in 4-chamber long-axis images during diastole is troublesome, because of the opening of the mitral valve.MethodsThis work presents a method for segmentation of the left ventricle in dynamic 2D-echo 4-chamber long-axis images over the complete cardiac cycle. The proposed algorithm is based on classic image processing techniques, including time-averaging and wavelet-based denoising, edge enhancement filtering, morphological operations, homotopy modification, and watershed segmentation. The proposed method is semi-automatic, requiring a single user intervention for identification of the position of the mitral valve in the first temporal frame of the video sequence. Image segmentation is performed on a set of dynamic 2D-echo images collected from an examination covering two consecutive cardiac cycles.ResultsThe proposed method is demonstrated and evaluated on twelve healthy volunteers. The results are quantitatively evaluated using four different metrics, in a comparison with contours manually segmented by a specialist, and with four alternative methods from the literature. The methods intra- and inter-operator variabilities are also evaluated.ConclusionsThe proposed method allows the automatic construction of the area variation curve of the left ventricle corresponding to a complete cardiac cycle. This may potentially be used for the identification of several clinical parameters, including the area variation fraction. This parameter could potentially be used for evaluating the global systolic function of the left ventricle.

Improved segmentation of echocardiographic images using fusion of images from different cardiac cycles

In this work, an algorithm for the detection of the left ventricular border in two-dimensional long axis echocardiographic images is presented. In its preprocessing stage, images fusion was applied to a sequence of images composed of three cardiac cycles. This method exploits the similarity of corresponding frames from different cycles and produces contrast enhancement in the left ventricular boundary. This result improves the performance of the segmentation stage which is based on watershed transformation. The obtained left ventricle border is quantitatively and qualitatively compared with contours manually segmented by a cardiologist, and with results obtained using seven different techniques from the literature.