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

NONINVASIVE POWER SPECTRUM ANALYSIS OF RADIAL PRESSURE WAVEFORM FOR ASSESSMENT OF VASCULAR SYSTEM

Wrist pulse diagnosis has been used in traditional Chinese medicine (TCM) for thousands of years, because pulse pressure signal contains a large number of physiological and pathological information of people. In this research, a systematic approach was proposed to analyze the computerized radial pressure waveform, with the focus placed on the power spectrum. We gained the power spectrum by using a modified fast Fourier transform, and the power-spectral characteristics were analyzed and compared. The analyzing program calculated the first peak frequency (F1) and the second peak (F2) automatically, and gained the time of phase shift between two frequencies. They could provide a simple noninvasive means for studying changes in the elastic properties of the vascular system depending on the age and the disease. Namely, the frequency analysis of radial pressure waveform gives new insight into the dynamics of cardiovascular system.

An enhanced algorithm for knee joint sound classification using feature extraction based on time-frequency analysis

Vibroarthrographic (VAG) signals, generated by human knee movement, are non-stationary and multi-component in nature and their time-frequency distribution (TFD) provides a powerful means to analyze such signals. The objective of this paper is to improve the classification accuracy of the features, obtained from the TFD of normal and abnormal VAG signals, using segmentation by the dynamic time warping (DTW) and denoising algorithm by the singular value decomposition (SVD). VAG and knee angle signals, recorded simultaneously during one flexion and one extension of the knee, were segmented and normalized at 0.5 Hz by the DTW method. Also, the noise within the TFD of the segmented VAG signals was reduced by the SVD algorithm, and a back-propagation neural network (BPNN) was used to classify the normal and abnormal VAG signals. The characteristic parameters of VAG signals consist of the energy, energy spread, frequency and frequency spread parameter extracted by the TFD. A total of 1408 segments (normal 1031, abnormal 377) were used for training and evaluating the BPNN. As a result, the average classification accuracy was 91.4 (standard deviation +/-1.7) %. The proposed method showed good potential for the non-invasive diagnosis and monitoring of joint disorders such as osteoarthritis.

Screening of knee-joint vibroarthrographic signals using statistical parameters and radial basis functions

Externally detected vibroarthrographic (VAG) signals bear diagnostic information related to the roughness, softening, breakdown, or the state of lubrication of the articular cartilage surfaces of the knee joint. Analysis of VAG signals could provide quantitative indices for noninvasive diagnosis of articular cartilage breakdown and staging of osteoarthritis. We propose the use of statistical parameters of VAG signals, including the form factor involving the variance of the signal and its derivatives, skewness, kurtosis, and entropy, to classify VAG signals as normal or abnormal. With a database of 89 VAG signals, screening efficiency of up to 0.82 was achieved, in terms of the area under the receiver operating characteristics curve, using a neural network classifier based on radial basis functions.

Modified local discriminant bases algorithm and its application in analysis of human knee joint vibration signals

Knee joint disorders are common in the elderly population, athletes, and outdoor sports enthusiasts. These disorders are often painful and incapacitating. Vibration signals [vibroarthrographic (VAG)] are emitted at the knee joint during the swinging movement of the knee. These VAG signals contain information that can be used to characterize certain pathological aspects of the knee joint. In this paper, we present a noninvasive method for screening knee joint disorders using the VAG signals. The proposed approach uses wavelet packet decompositions and a modified local discriminant bases algorithm to analyze the VAG signals and to identify the highly discriminatory basis functions. We demonstrate the effectiveness of using a combination of multiple dissimilarity measures to arrive at the optimal set of discriminatory basis functions, thereby maximizing the classification accuracy. A database of 89 VAG signals containing 51 normal and 38 abnormal samples were used in this study. The features extracted from the coefficients of the selected basis functions were analyzed and classified using a linear-discriminant-analysis-based classifier. A classification accuracy as high as 80% was achieved using this true nonstationary signal analysis approach.

Vibration arthrometry in patients with knee joint disorders

Physiological patellofemoral crepitus (PPC) is the vibration signal produced by the knee joint during slow motion (less than 5 degrees per second), which can be measured by vibration arthrometry (VAM). By using the autoregressive (AR) model for the PPC signals of patients with knee osteoarthritis, the study analyzes the PPC signals to evaluate the condition of patellar-femoral joint cartilage. Accordingly, we can divide osteoarthritis into three types, type 1: the cartilage of patellar-femoral joint is intact, the osteoarthritis found in the femoral-tibial joint surface; type 2: degeneration occurs in the surface cartilage of both the femoral-tibial joint and the femoral trochlea, but not on the patellar surface; type 3: both patellar-femoral and femoral-tibial joints have osteoarthritis. For the analysis, the intraclass distance of AR coefficients and spectral power ratio of dominant poles are adopted. Based on the proposed method, two cases of type 1, six of type 2, and 28 of type 3 were found in 36 cases of knee osteoarthritis. This is in agreement with the operative findings. For comparison, the PPC signals of 10 subjects with normal knees (without pain or wound history) were also measured. The results of analysis of the 10 normal subjects were consistent and clearly differentiable from those of the osteoarthritis patients. Therefore, the proposed method is efficient for the analysis of the condition of patellar-femoral joint cartilage and VAM may become an alternative way of noninvasive diagnosis of knee osteoarthritis.

Vibration arthrometry in the patients with failed total knee replacement

This is a preliminary research on the vibration arthrometry of artificial knee joint in vivo. Analyzing the vibration signals measured from the accelerometer on patella, there are two speed protocols in knee kinematics: 1) 2 degrees/s, the signal is called "physiological patellofemoral crepitus (PPC)", and 2) 67 degrees/s, the signal is called "vibration signal in rapid knee motion". The study has collected 14 patients who had revision total knee arthroplasty due to prosthetic wear or malalignment represent the failed total knee replacement (FTKR), and 12 patients who had just undergone the primary total knee arthroplasty in the past two to six months and have currently no knee pain represent the normal total knee replacement (NTKR). FTKR is clinically divided into three categories: metal wear, polyethylene wear of the patellar component, and no wear but with prosthesis malalignment. In PPC, the value of root mean square (rms) is used as a parameter; in vibration signals in rapid knee motion, autoregressive modeling is used for adaptive segmentation and extracting the dominant pole of each signal segment to calculate the spectral power ratios in f < 100 Hz and f > 500 Hz. It was found that in the case of metal wear, the rms value of PPC signal is far greater than a knee joint with polyethylene wear and without wear, i.e., PPC signal appears only in metal wear. As for vibration signals in rapid knee motion, prominent time-domain vibration signals could be found in the FTKR patients with either polyethylene or metal wear of the patellar component. We also found that for normal knee joint, the spectral power ratio of dominant poles has nearly 80% distribution in f < 100 Hz, is between 50% and 70% for knee with polyethylene wear and below 30% for metal wear, whereas in f > 500 Hz, spectral power ratio of dominant poles has over 30% distribution in metal wear but only nonsignificant distribution in polyethylene wear, no wear, and normal knee. The results show that vibration signals in rapid knee motion can be used for effectively detecting polyethylene wear of the patellar component in the early stage, while PPC signals can only be used to detect prosthetic metal wear in the late stage.

Evaluation of linear diaphragm-chest expansion models for magnetic resonance imaging motion artifact correction

The efficacy of Fourier analysis, autoregressive with exogenous input (ARX) and adaptive models to estimate diaphragm position from respiratory belt signal (a measure of chest expansion) was evaluated for the purpose of correcting respiratory motion artifacts in magnetic resonance imaging (MRI). Respiratory belt signal and diaphragm position data were obtained simultaneously during one-dimensional MRI scans with sampling intervals of 100 ms for 128 s (1280 samples). The models were trained using the first 512 data samples for the Fourier method and the first 640 samples for the ARX and adaptive methods. The remaining samples were used as a test set for evaluating the models. Both ARX and adaptive methods produced more accurate results than the Fourier method as reflected by the normalized mean square error (NMSE) and correlation coefficient (R) between the estimated and actual diaphragm position during normal breathing (P < 0.05). However, all three models had difficulty modeling diaphragm positions during breathing plateaus.

Marked regularity models

We present a generalization of the regularity model, which is a stationary point process model describing how often and how regularly a random "event" occurs. The generalization allows the amplitude of each event to be a sample from a random process. First, we developed closed-form approximations of the power spectra of data segments; then we examined the accuracy of a procedure that estimates the regularity and mark process parameters by minimizing the error between measured spectra and the approximations. We found the following. In the absence of measurement noise, joint estimation of both mark and regularity parameters is accurate only if the ratio of the square of the mean of the marks to the variance of the marks (the SMNPR) is small. Marginal estimation of the regularity process parameters can be accurate if the mark process is taken into account by minimizing overall parameters; the accuracy then depends on both measurement noise and SMNPR. Error in the marginal estimation of the regularity process parameters will be inversely proportional to the SMNPR if the marks are ignored by minimizing only with respect to the regularity parameters, so ignoring the marks can cause a substantial degradation in accuracy when the SMNPR is small. We illustrate these findings with an acoustic scattering example in which simulated ultrasound measurements of tissue samples are characterized by their description in the parameter space.

Physical mechanisms involved in the genesis of temporomandibular joint sounds

Several different mechanisms are potentially capable of generating sounds in the temporomandibular joint (TMJ). These include impact, sliding and stick-slip friction, fluid dynamic effects and the release of elastic strain energy. It is the aim of this paper to provide a framework with which to separate sounds resulting from the different underlying causes. Each mechanism is described and its relevance to TMJ sounds and clinical significance discussed. Since it is not possible to observe these mechanisms in vivo the arguments are based mainly on analogies which are used to make predictions of the characteristic acoustic signatures of the sounds produced by these different mechanisms. In particular the changes in the characteristics of the sounds as parameters such as mandibular speed and loading are stressed. It is suggested that single short duration sounds (clicks) are due to impact, multiple short duration sounds (creaks) to stick-slip friction and defects of form and long duration sounds (crepitus) to simple sliding friction. Several other mechanisms which have no obvious clinical significance but which are capable of producing similar sounds are also described and methods of distinguishing them from the sounds that do have clinical implications are discussed.

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.

Characterization of sounds emanating from the human temporomandibular joints

Sounds from the temporomandibular joint were recorded on audiotape from 238 individuals by placing microphones in both ears. The recordings were later digitized at a sample rate of 1.7 kHz with 10-bit resolution and stored on computer disk. At least two open-close cycles were assessed from each individual; 2707 different individual sounds were analysed in the time and frequency domains. The sounds were classified as: (a) single, short duration (clicks), (b) multiple, short-duration (creaks) and (c) long duration (crepitus). The sounds were further subclassified into either high or low amplitude by (i) the attack, which produced hard and soft categories and (ii) comparing the amplitude between sides-bilateral sounds were those with amplitudes differing by < 40 mV; the rest were unilateral. To establish the robustness of the classification 42 acoustic events were selected to be classified visually by three observers on two separate occasions. Intraobserver agreement was 82% (kappa = 0.75) while interobserver agreement was 60% (kappa = 0.71). Statistically significant differences were noted between all classifications of sound. These were most marked in the time domain. A simple, automated classification scheme was devised that was capable of categorizing the sounds with 82% agreement (kappa = 0.71) compared to a human observer.

Screening of vibroarthrographic signals via adaptive segmentation and linear prediation modeling

This paper proposes a noninvasive method to diagnose chondromalacia patella at its early stages by recording knee vibration signals (also known as vibroarthrographic or VAG signals) over the mid-patella during normal movement. An adaptive segmentation method was developed to segment the nonstationary VAG signals. The least squares modeling method was used to reduce the number of data samples to a few model parameters. Model parameters along with a few clinical parameters and a signal variability parameter were then used as discriminant features for screening VAG signals by applying logistic and discriminant algorithms. The system was trained using ten normal and eight abnormal signals. It correctly screened a separate test set of ten normal and eight abnormal signals except for one normal signal. The proposed method should find use as an alternative technique for diagnosis of knee joint pathology or as a test before arthroscopy or major knee surgery.

Adaptive cancellation of muscle contraction interference in vibroarthrographic signals

Vibroarthrography (VAG) is an innovative, objective, non-invasive technique for obtaining diagnostic information concerning the articular cartilage of a joint. Knee VAG signals can be detected using a contact sensor over the skin surface of the knee joint during knee movement such as flexion and/or extension. These measured signals, however, contain significant interference caused by muscle contraction that is required for knee movement. Quality improvement of VAG signals is an important subject, and crucial in computer-aided diagnosis of cartilage pathology. While simple frequency domain high-pass (or band-pass) filtering could be used for minimizing muscle contraction interference (MCI), it could eliminate possible overlapping spectral components of the VAG signals. In this work, an adaptive MCI cancellation technique is presented as an alternative technique for filtering VAG signals. Methods of measuring the VAG and reference signals (MCI) are described, with details on MCI identification, characterization, and step size optimization for the adaptive filter. The performance of the method is evaluated by simulated signals as well as signals obtained from human subjects under isotonic contraction.