Localization of knee joint cartilage pathology by multichannel vibroarthrography

VibroCV: a computer vision-based vibroarthrography platform with possible application to Juvenile Idiopathic Arthritis

Vibroarthrography, a method for interpreting the sounds emitted by a knee during movement, has been studied for several joint disorders since 1902. However, to our knowledge, the usefulness of this method for management of Juvenile Idiopathic Arthritis (JIA) has not been investigated. To study joint sounds as a possible new biomarker for pediatric cases of JIA we designed and built VibroCV, a platform to capture vibroarthrograms from four accelerometers; electromyograms (EMG) and inertial measurements from four wireless EMG modules; and joint angles from two Sony Eye cameras and six light-emitting diodes with commercially-available off-the-shelf parts and computer vision via OpenCV. This article explains the design of this turn-key platform in detail, and provides a sample recording captured from a pediatric subject.

Preliminary study of optimal measurement location on vibroarthrography for classification of patients with knee osteoarthritis

[Purpose] The aims of the present study were to investigate the most suitable location for vibroarthrography measurements of the knee joint to distinguish a healthy knee from knee osteoarthritis using Wavelet transform analysis. [Subjects and Methods] Participants were 16 healthy females and 17 females with severe knee osteoarthritis. Vibroarthrography signals were measured on the medial and lateral epicondyles, mid-patella, and tibia using stethoscopes with a microphone while subjects stood up from a seated position. Frequency and knee flexion angles at the peak wavelet coefficient were obtained. [Results] Peak wavelet coefficients at the lateral condyle and tibia were significantly higher in patients with knee osteoarthritis than in the control group. Knee joint angles at the peak wavelet coefficient were smaller (more extension) in the osteoarthritis group compared to the control group. The area under the receiver operating characteristic curve on tibia assessment with the frequency and knee flexion angles was higher than at the other measurement locations (both area under the curve: 0.86). [Conclusion] The tibia is the most suitable location for classifying knee osteoarthritis based on vibroarthrography signals.

Synthesis of vibroarthrographic signals in knee osteoarthritis diagnosis training

Background

Vibroarthrographic (VAG) signals are used as useful indicators of knee osteoarthritis (OA) status. The objective was to build a template database of knee crepitus sounds. Internships can practice in the template database to shorten the time of training for diagnosis of OA.

Methods

A knee sound signal was obtained using an innovative stethoscope device with a goniometer. Each knee sound signal was recorded with a Kellgren–Lawrence (KL) grade. The sound signal was segmented according to the goniometer data. The signal was Fourier transformed on the correlated frequency segment. An inverse Fourier transform was performed to obtain the time-domain signal. Haar wavelet transform was then done. The median and mean of the wavelet coefficients were chosen to inverse transform the synthesized signal in each KL category. The quality of the synthesized signal was assessed by a clinician.

Results

The sample signals were evaluated using different algorithms (median and mean). The accuracy rate of the median coefficient algorithm (93 %) was better than the mean coefficient algorithm (88 %) for cross-validation by a clinician using synthesis of VAG.

Conclusions

The artificial signal we synthesized has the potential to build a learning system for medical students, internships and para-medical personnel for the diagnosis of OA. Therefore, our method provides a feasible way to evaluate crepitus sounds that may assist in the diagnosis of knee OA.

An acoustical evaluation of knee sound for non-invasive screening and early detection of articular pathology

Knee sound signals generated by knee movement are sometimes associated with degeneration of the knee joint surface and such sounds may be a useful index for early disease. In this study, we detected the acoustical parameters, such as the fundamental frequency (F0), mean amplitude of the pitches, and jitter and shimmer of knee sounds, and compared them according to the pathological conditions. Six normal subjects (4 males and 2 females, age: 28.3 ± 2.3 years) and 11 patients with knee problems were enrolled. The patients were divided into the 1st patient group (5 males and 1 female, age: 30.2 ± 10.3 years) with ruptured wounds of the meniscus and 2nd patient group (2 males and 3 females, age: 42.1 ± 16.2 years) with osteoarthritis. The mean values of F0, jitter and shimmer of the 2nd patient group were larger than those of the normal group, whereas those of the 1st patient group were smaller (p < 0.05). Also, the F0 and jitter in the standing position were larger than those in the sitting position in both the 1st and 2nd patient groups (p < 0.05). These results showed good potential for the non-invasive diagnosis and early detection of articular pathologies via an auscultation.

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.

Electrical impedance signal analysis in assessing the possibility of non-invasive diagnosis of knee osteoarthritis

Knee osteoarthritis (OA) is a degenerating disorder that leads to pain, disability and dependence. Although significant numbers of elderly people are affected by this irreversible damage, not many non-invasive methods have been found that can detect onset of OA. The traditional x-ray has the disadvantage of detecting a problem only after many changes have taken place. Others, such as MRI and ultrasound, are either expensive or unsuitable for mass screening and repeated use. In this paper, an attempt has been made to study the usefulness of electrical impedance plethysmography (EIP) in non-invasive diagnosis of knee OA. In two experiments on 10 OA knees and eight control knees in groups aged 45 - 65 years (OA group: 62.40 +/- 3.47 years, controls: 53.38 +/- 8.55 years), knee swing (active flexion and extension of leg in sitting position, KS) and normal walking (WN) electrical impedance changes (DeltaZ) around the knee were analysed. The results indicate that there is significant difference in amplitudes of signals. Difference in mean of variances of two groups was significant (p < 0.05) for KS and WN. The difference in the mean rms values was also significant (p < 0.05) for KS and WN. Impedance changes suggest that EIP signal around the knee have the potential for non-invasive diagnosis of knee OA.

Do shoulder vibration signals vary among asymptomatic volunteers?

Numerous studies document vibrations emanating from joints during active or passive motion. It has been proposed these vibrations, termed vibroarthrographic signals, are associated with changes in the shape or quality of tissues in and around the joint. Vibroarthrographic signals in articular joints have been tested to correlate a particular signal with a particular feature of a joint such as a specific lesion. Because of the limited morphologic changes noted in dominant and nondominant articular joints, we hypothesized shoulder vibroarthrographic signals would be similar between subjects. We determined vibroarthrographic signals in young, adult, asymptomatic volunteers evaluated by 21 different active physician-assisted physical examination tests. Comparisons of data from both shoulders with a two-sample statistical test and a neural network demonstrated difficulty distinguishing the dominant and nondominant shoulder. Four percent of the comparisons were different, and the sensitivity of the neural network averaged 50% for most physical examination tests when classifying shoulder signals as dominant or non-dominant. Our findings suggest future studies investigating vibroarthrographic signals from symptomatic shoulders can be compared with asymptomatic shoulders from young patients with little regard to limb dominance for most physical examination tests.

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 (TFD's) provide powerful means to analyze such signals. The objective of this paper is to construct adaptive TFD's 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 TFD's. 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.

Parametric representation and screening of knee joint vibroarthrographic signals

We 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, we 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%.

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.