G.D. Bell

Adaptive time-frequency analysis of knee joint vibroarthrographic signals for noninvasive screening of articular cartilage pathology

Vibroarthrographic (VAG) signals emitted by human knee joints are nonstationary and multicomponent in nature; time-frequency distributions (TFDs) provide powerful means to analyze such signals. The objective of this paper is to construct adaptive TFDs of VAG signals suitable for feature extraction. An adaptive TFD was constructed by minimum cross-entropy optimization of the TFD obtained by the matching pursuit decomposition algorithm. Parameters of VAG signals such as energy, energy spread. frequency, and frequency spread were extracted from their adaptive TFDs. The parameters carry information about the combined TF dynamics of the signals. The mean and standard deviation of the parameters were computed, and each VAG signal was represented by a set of just six features. Statistical pattern classification experiments based on logistic regression analysis of the parameters showed an overall normal/abnormal screening accuracy of 68.9% with 90 VAG signals (51 normals and 39 abnormals), and a higher accuracy of 77.5% with a database of 71 signals with 51 normals and 20 abnormals of a specific type of patellofemoral disorder. The proposed method of VAG signal analysis is independent of joint angle and clinical information, and shows good potential for noninvasive diagnosis and monitoring of patellofemoral disorders such as chondromalacia patella.

Analysis of knee vibration signals using linear prediction

The possibility of developing a safe, objective, noninvasive method for early detection, localization, and quantification of cartilage pathology in the knee, based on an analysis of vibrations produced by joint surfaces rubbing against one another during normal movement, is investigated. In particular, the method of modeling by linear prediction is used for adaptive segmentation and parameterization of knee vibration signals. Dominant poles are extracted from the model system function for each segment based on its energy contributions and bandwidths. These dominant poles represent the dominant features of the signal segments in the spectral domain. Two-dimensional feature vectors are then constructed using the first dominant pole and the ratio of power in the 40-120-Hz band to the total power of the segment. The potential use of this method to distinguish between vibrations produced by normal volunteers and patients known to have cartilage pathology (chondromalacia) is discussed.<<ETX>>

Parametric representation and screening of knee joint vibroarthrographic signals

The authors have been investigating analysis of knee joint vibration or vibroarthrographic (VAG) signals as a potential tool for noninvasive diagnosis and monitoring of cartilage pathology. In this paper, they present a comprehensive comparative study of different parametric representations of VAG signals. Dominant poles and cepstral coefficients were derived from autoregressive models of adaptively segmented VAG signals. Signal features and a few clinical features were used as feature vectors in pattern classification experiments based on logistic regression analysis and the leave-one-out method. The results using 51 normal and 39 abnormal signals indicated the superior performance of cepstral coefficients in VAG signal classification with an accuracy rate of 75.6%. With 51 normal and 20 abnormal signals limited to chondromalacia patella, cepstral coefficients again gave the highest accuracy rate of 85.9%.

A comparative study of simultaneous vibromyography and electromyography with active human quadriceps

Vibromyographic (VMG) signals, which are low-frequency vibration signals generated during muscle contraction, were studied in comparison with electromyographic (EMG) signals recorded simultaneously during isometric contraction of the human quadriceps muscles. It was found that the VMG and EMG under the same conditions on the same muscle group are, in general, equally sensitive to the levels of muscle contraction. Results show that the RMS value of the VMG signal increases linearly, in a manner similar to the EMG RMS/% maximal voluntary contraction (MVC) relationship, with increasing muscle contraction levels. Furthermore, the study indicates that the averaged mean frequency and peak frequency of the VMG signals are much lower than those of the EMG signals. The results also indicate that certain relationships for the VMG signal, like those of the EMG, may reflect muscle activation patterns, while the difference in frequency content between the VMG and the EMG reflects the morphological differences between the mechanical and electrical responses to muscle activation. The signals exhibit a feature of joint angle dependence.<<ETX>>

Adaptive filtering, modelling and classification of knee joint vibroarthrographic signals for non-invasive diagnosis of articular cartilage pathology.

Interpretation of vibrations or sound signals emitted from the patellofemoral joint during movement of the knee, also known as vibroarthrography (VAG), could lead to a safe, objective, and non-invasive clinical tool for early detection, localisation, and quantification of articular cartilage disorders. In this study with a reasonably large database of VAG signals of 90 human knee joints (51 normal and 39 abnormal), a new technique for adaptive segmentation based on the recursive least squares lattice (RLSL) algorithm was developed to segment the non-stationary VAG signals into locally-stationary components; the stationary components were then modelled autoregressively, using the Burg-Lattice method. Logistic classification of the primary VAG signals into normal and abnormal signals (with no restriction on the type of cartilage pathology) using only the AR coefficients as discriminant features provided an accuracy of 68.9% with the leave-one-out method. When the abnormal signals were restricted to chondromalacia patella only, the classification accuracy rate increased to 84.5%. The effects of muscle contraction interference (MCI) on VAG signals were analysed using signals from 53 subjects (32 normal and 21 abnormal), and it was found that adaptive filtering of the MCI from the primary VAG signals did not improve the classification accuracy rate. The results indicate that VAG is a potential diagnostic tool for screening for chondromalacia patella.

Localization of knee joint cartilage pathology by multichannel vibroarthrography

This paper proposes non-invasive techniques to localize sound or vibroarthrographic (VAG) signal sources in human knee joints. VAG signals from normal subjects, patients who subsequently underwent arthroscopy, and cadavers with arthroscopically-created lesions, obtained by stimulation with a finger tap over the mid-patella and swinging movement of the leg, were analyzed for time delays using cross-correlation functions for source localization. Correct results were obtained for 13 of the 14 subjects tested by finger stimulation, and for 11 of the 12 subjects whose VAG signals during swinging movement were analyzed. The techniques could be valuable in the diagnosis and treatment of knee pathology before and after joint surgery or drug therapy.

Mathematical modeling and spectrum analysis of the physiological patello-femoral pulse train produced by slow knee movement

A mathematical model for knee vibration signals, in particular the physiological patello-femoral pulse (PFP) train produced by slow knee movement, is presented. It demonstrated that the repetition rate of the physiological PFP train introduces repeated peaks in the power spectrum, and that it affects the spectrum mainly at low frequencies. The theoretical results also show that the spectral peaks at multiples of the PFP repetition rate become more evident when the variance of the interpulse interval (IPI) is small, and that these spectral peaks shift toward higher frequencies with increasing PFP repetition rates. To evaluate the mathematical model, a simulation algorithm which generate PFP signals with adjustable repetition rate and IPI variance was developed. Results of simulations and analysis of signals recorded from human subjects support the mathematical models prediction that the IPI statistics play a very significant role in determining the low-end power spectrum of the physiological PFP signal. Evidence is presented to show that ignoring these IPI statistics will affect the interpretation of the spectrum.<<ETX>>

Muscle Contraction Interference In Acceleration Vibroarthrography

Vibroarthrography (VAG) is a noninvasive technique to detect vibration signals from the knee joint for the diagnosis of articular cartilage diseases. To improve the quality of VAG signals and to extract relevant information from them, possible sources of artifacts such as muscle contraction interference (MCI) were investigated by using a multi-channelvibration detection system. Analysis of the signals indicates that signal power and median frequency of VAG signals are affected by MCI. Our findings suggest that monitoring and reduction of MCI is necessary in order for vibroarthrography to be clinically useful.

Analysis of knee joint sound signals

The authors have conducted studies on the recording and analysis of knee joint sound and angle signals using a microcomputer system. Short-time Fourier analysis techniques were used to study trends in the frequency content of the signals. Signals obtained from 16 patients before arthroscopy were analyzed. Signals corresponding to various degrees of chondromalacia and meniscal lesions demonstrated distinct characteristics, which should aid in automated analysis and diagnosis.<<ETX>>

Sonification of knee-joint vibration signals

Sounds generated due to rubbing of knee-joint surfaces may be a potential tool for noninvasively assessing articular cartilage degeneration. In this paper, an attempt is made to perform computer-assisted auscultation of knee joints by auditory display (AD) of the vibration signals (also known as vibroarthrographic or VAG signals) emitted during active movement of the leg. The AD technique is based on a sonification algorithm, in which the instantaneous mean frequency and envelope of the VAG signals were used in improving the potential diagnostic quality of VAG signals. Auditory classification experiments were performed by two orthopedic surgeons with a database of 37 VAG signals that includes 19 normal and 18 abnormal cases. Sensitivities of 31% and 83% were obtained with direct playback and the sonification method, respectively.