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Analysis of knee-joint vibration sounds, also known as vibroarthrographic (VAG) signals, could lead to a noninvasive clinical tool for early detection of knee-joint pathology. In this paper, we employed the wavelet matching pursuit (MP) decomposition and signal variability for time-frequency domain and time-domain analysis of VAG signals. The number of wavelet MP atoms and the number of significant...
Knee-joint sounds or vibroarthrographic (VAG) signals contain diagnostic information related to the roughness, softening, breakdown, or the state of lubrication of the articular cartilage surfaces. Objective analysis of VAG signals provides features for pattern analysis, classification, and noninvasive diagnosis of knee-joint pathology of various types. We propose the use of several parameters related...
The electrocardiogram (ECG) is the routinely used biomedical signal for diagnosis of cardiovascular diseases, and the removal of noise in ambulatory ECG recordings is essential in a number of clinical applications. In this paper, we present a Daubechies wavelet analysis method with a decomposition tree of level 5 (Wdb5)for analysis of noisy ECG signals. The implementation includes the procedures of...
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...
Vibrations emitted from a knee joint during flexion or extension are expected to be associated with pathological conditions in the joint. Externally detected vibroarthrographic (VAG) signals may be useful indicators of roughness, softening, breakdown, or the state of lubrication of the articular cartilage surfaces of the joint. Computer-aided analysis of VAG signals could provide quantitative indices...
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...
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...
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