Adaptive cancellation of muscle contraction interference in vibroarthrographic signals

Acoustical features as knee health biomarkers: A critical analysis

Acoustical knee health assessment has long promised an alternative to clinically available medical imaging tools, but this modality has yet to be adopted in medical practice. The field is currently led by machine learning models processing acoustical features, which have presented promising diagnostic performances. However, these methods overlook the intricate multi-source nature of audio signals and the underlying mechanisms at play. By addressing this critical gap, the present paper introduces a novel causal framework for validating knee acoustical features. We argue that current machine learning methodologies for acoustical knee diagnosis lack the required assurances and thus cannot be used to classify acoustic features as biomarkers. Our framework establishes a set of essential theoretical guarantees necessary to validate this claim. We apply our methodology to three real-world experiments investigating the effect of researchers' expectations, the experimental protocol, and the wearable employed sensor. We reveal latent issues such as underlying shortcut learning and performance inflation. This study is the first independent result reproduction study in acoustical knee health evaluation. We conclude by offering actionable insights that address key limitations, providing valuable guidance for future research in knee health acoustics.

Vibroarthrography, arthrophonography — methods for non-invasive detection of the knee cartilage damage

Phonoarthrography, vibration arthrography are non-invasive methods for assessing the condition of cartilage and the knee joint as a whole based on the sounds made by the joint movement. Acoustic sensors (accelerometers, microphones) are attached to the knee to measure the knee joint noise both in control groups (young adults and elderly subjects) and in patients with knee osteoarthropathies. Different authors propose different methods for attaching sensors, documenting and analyzing the joint sounds. The identified specific features allowed distinguishing between asymptomatic knee joints and those with osteoarthropathies. Acoustic signals were recorded and processed, and their frequency characteristics were determined and classified. The classification effectiveness correlated with the existing diagnostic tests and hence phonoarthrography and vibration arthrography can be qualified as a useful diagnostic aid.

Two-Wired Current Modulator Active Electrode for Ambulatory Biosignal Recording

This paper presents an innovative current modulator active electrode (CMAE) with only two outgoing wires that reduces cost and complexity of wearable bioamplifiers based on dry electrodes. The CMAE can be considered as an operational transconductance amplifier, which modulates the supply current by input voltage signal through its two supply rails. The CMAE prototype is implemented with only two discrete op-amps for ECG and EEG recording with current consumption of 60 μA and 100 μA, respectively. The CMAE's built-in preamplifier has utilized a dc feedback loop, instead of the conventional dc servo loop, which suppresses electrode offset in almost rail-to-rail. Total input-referred noise voltages of the ECG and EEG type CMAEs are 3.9 μV[Formula: see text] and 0.7 μV _RMS, respectively, in 0.5-100 Hz bandwidth. By using bipolar pseudoresistors, sub-hertz high-pass corner frequency but with fast settling time is achieved. The common-mode noise due to the disparate channels imbalances is digitally rejected by using an online adaptive noise canceler, which boosts the common-mode rejection ratio up to 120 dB with assisting the driven right leg circuit.

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.

ARTICULAR SOUND AND CLINICAL STAGES IN KNEE ARTHROPATHY

Objective: To clarify the pathophysiology of knee arthropathy, articular sound in the knee joint was recorded using an accelerometer, vibroarthrography (VAG), during standing-up and sitting-down movements in patients with osteoarthropathy (OA) of the knees. Methods: VAG signals and angular changes of the knee joint during standing-up and sitting-down movements were recorded in patients with OA, including 17 knees with OA at Kellgren–Lawrence stage I and II, 16 knees with OA at III and IV stages, and 20 knees of age-matched control subjects. Results: The level of VAG signals was greater in knees with a higher stage of OA at 50–99 and 100–149 Hz among the groups (ANOVA with Tukey–Kramer multiple comparisons test, p < 0.01). The VAG signals did not correlate with WOMAC-pain or physical scores. Conclusions: We considered that the increase in VAG signals in these ranges of frequency corresponded with pathological changes of OA, but not self-reported clinical symptoms. This method of VAG can be used by clinicians during interventions to obtain pathological information regarding structural changes of the knee joint.

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.

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.

Using system identification to model the transmission of vibroarthrographic signals

Vibroarthrographic signals have been proposed as a noninvasive tool for the diagnosis of joint injury. Models of VAG generation and transmission are required before application of this technique can begin. An experiment has been designed and performed to estimate sound transmission in the human knee at set joint angles. Linear frequency domain models and linear and nonlinear time domain models were estimated from the experimental data. Linear models with high accuracy were identified for knees at an angle of 90/ degrees . Models identified from angles below 90 degrees had relatively low accuracy.

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.

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.

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.

Automatic de-noising of knee-joint vibration signals using adaptive time-frequency representations

A novel de-noising method for improving the signal-to-noise ratio of knee-joint vibration signals (also known as vibro-arthrographic (VAG) signals) is proposed. The de-noising methods considered are based on signal decomposition techniques, such as wavelets, wavelet packets and the matching pursuit (MP) method. Performance evaluation with synthetic signals simulated with the characteristics expected of VAG signals indicates good de-noising results with the MP method. Statistical pattern classification of non-stationary signal features extracted from time-frequency distributions of 37 (19 normal and 18 abnormal) MP method-de-noised VAG signals shows a sensitivity of 83.3%, a specificity of 84.2% and an overall accuracy of 83.8%.

Preamplifier with a second-order high-pass filtering characteristic

A new preamplifier for suppressing low-frequency interference is presented. The proposed preamplifier, with its front end being implemented by an instrumentation amplifier, enjoys the following advantages: differential high-pass filtering, high input impedance, high common--mode rejection ratio and low passive sensitivity. This circuit can be realized with commercial operational amplifiers with enough phase margin, or fabricated in a chip for practical measurement of physiological signals.

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.

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.

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.