The Effect of External Loads and Cyclic Loading on Normal Patellofemoral Joint Signals.

Pain over the anterior portion of the knee joint is a common clinical complaint. A condition known as 'chondromalacia patella' (softening of the cartilage under the patella), which frequently causes anterior knee pain is difficult to diagnose and monitor. Vibrations detected by a contact transducer over the patellofemoral joint may be useful in the assessment of chondromalacia patella. This paper utilised this technique known as vibroarthrography (VAG), to study two potential sources of variability of the normal patellofemoral joint signal. The effect of increased muscular force on the VAG signal was measured by externally loading the joint. The effect of load history (cyclic loading) on the VAG signal was determined by comparing signals before, during, and after application of weights under similar cyclic loading conditions. Results indicated that external loading of the patellofemoral joint caused only minor signal variation. Cyclical loading of the joint, on the other hand, was determined to be a major source of variability of the normal patellofemoral joint signal, which must be controlled in future VAG tests

An algorithm for evaluating the performance of adaptive filters for the removal of artifacts in ECG signals

Filtering electrocardiogram (ECG) signals calls for a filter whose impulse response can be automatically adjusted according to the varying characteristics of the signal and artifacts. In order to eliminate effectively the artifacts in ECG signals, we propose the unbiased linear artificial neural network (ULANN) as a new type of adaptive filter. This paper compares the performance of the ULANN filter with the prevailing least-mean-squares (LMS) and recursive-least-squares (RLS) adaptive filters, for the removal of artifacts in noisy ECG signals. The measures of performance include the root-mean-squared error, a normalized correlation coefficient (NCC), and entropy. A template derived from each ECG signal is used as a reference to derive the measures. The NCC values for the ULANN, LMS, and RLS filter, averaged over 22 ECG signals, are 0.9956 +/- 0.0022, 0.9948 +/- 0.0020, and 0.9940 +/- 0.0026, respectively. The results indicate that the ULANN filter provides filtered signals with the highest waveshape fidelity among the three filters studied

An unbiased linear artificial neural network with normalized adaptive coefficients for filtering noisy ECG signals

The electrocardiogram (ECG) is the most commonly used signal for diagnostic purposes in medicine. The adaptive filtering technique is suited for filtering ECG signals, which are inherently nonstationary. In this paper, we propose a novel neural-network-based adaptive filter to eliminate high-frequency random noise in ECG signals. We make use of a linear artificial neural network (ANN) with delayed values of the ECG time series as the filter inputs. The ANN does not contain a bias in its summation unit, and the coefficients are normalized. During the learning process, the normalized coefficients are used in the steepest-descent algorithm in order to achieve efficient online filtering of noisy ECG signals

Screening of knee-joint vibroarthrographic signals using probability density functions estimated with Parzen windows

Pathological conditions of knee joints have been observed to cause changes in the characteristics of vibroarthrographic (VAG) signals. Several studies have proposed many parameters for the analysis and classification of VAG signals; however, no statistical modeling methods have been explored to analyze the distinctions in the probability density functions (PDFs) between normal and abnormal VAG signals. In the present work, models of PDFs were derived using the Parzen-window approach to represent the statistical characteristics of normal and abnormal VAG signals. The Kullback-Leibler distance was computed between the PDF of the signal to be classified and the PDF models for normal and abnormal VAG signals. Additional statistical measures, including the mean, standard deviation, coefficient of variation, skewness, kurtosis, and entropy, were also derived from the PDFs obtained. An overall classification accuracy of 77.53%, sensitivity of 71.05%, and specificity of 82.35% were obtained with a database of 89 VAG signals using a neural network with radial basis functions with the leave-one-out procedure for cross validation. The screening efficiency was derived to be 0.8322, in terms of the area under the receiver operating characteristics curve. (C) 2009 Elsevier Ltd. All rights reserved

Filtering of noise in electrocardiographic signals using an unbiased and normalized adaptive artifact cancellation system

The electrocardiogram (ECG) is routinely used for the diagnosis of cardiovascular diseases. The removal of artifacts in ambulatory ECG recordings is essential in many biomedical applications. In this paper, we present the design of an unbiased linear filter with normalized weight coefficients in an adaptive artifact cancellation (UNAAC) system. We also develop a new weight coefficient adaptation algorithm that normalizes the filter coefficients, and utilize the steepest-descent algorithm to effectively cancel the artifacts present in ECG signals. The proposed UNAAC system was tested through experiments on the benchmark MIT-BIH database. Empirical results demonstrate that the UNAAC system can effectively eliminate two types of predominant artifacts: baseline wander and muscle-contraction artifact. Furthermore, the proposed UNAAC system achieved significantly higher signal-to-noise and signal-to-error ratios in the enhanced ECG signals, as compared with the normalized least-mean-square (NLMS) filter

Cancellation of artifacts in ECG signals using a normalized adaptive neural filter

Denoising electrocardiographic (ECG) signals is an essential procedure prior to their analysis. In this paper, we present a normalized adaptive neural filter (NANF) for cancellation of artifacts in ECG signals. The normalized filter coefficients are updated by the steepest-descent algorithm; the adaptation process is designed to minimize the difference between second-order estimated output values and the desired artifact-free ECG signals. Empirical results with benchmark data show that the adaptive artifact canceller that includes the NANF can effectively remove muscle-contraction artifacts and high-frequency noise in ambulatory ECG recordings, leading to a high signal-to-noise ratio. Moreover, the performance of the NANF in terms of the root-mean-squared error, normalized correlation coefficient, and filtered artifact entropy is significantly better than that of the popular least-mean-square (LMS) filter

Simple fractal method of assessment of histological images for application in medical diagnostics

We propose new method of assessment of histological images for medical diagnostics. 2-D image is preprocessed to form 1-D landscapes or 1-D signature of the image contour and then their complexity is analyzed using Higuchi's fractal dimension method. The method may have broad medical application, from choosing implant materials to differentiation between benign masses and malignant breast tumors

Material Density Distribution of a Radial Symmetric Product from a Single X-Ray Radiograph

X-ray digital tomographic methods may be classified according to the number of projections and the angular coverage required to obtain the density distribution of the object under study. At one extremity stands computerized tomography which employs multiple projections and wide angular coverage (± π). At the other extremity stand reconstruction methods employing a single projection. As the number of projections decreases, the information provided for the reconstruction becomes more incomplete. The decrease in the information content may sometimes be compensated by the use of a priori knowledge about the product and thus alleviate to some extent the ill-posedness of the problem [l–4]