Analysis and multiclass classification of pathological knee joints using vibroarthrographic signals

Kręcisz, Krzysztof; Bączkowicz, Dawid

doi:10.1016/j.cmpb.2017.10.027

Cited by 40 publications

(52 citation statements)

References 25 publications

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“…Both variability (VMS) and amplitude (R4) of representative signals are characterized by low values. Especially the low values of the R4 parameter that indicate a lack of repetitive large peaks, whose presence often corresponds with macroscopic joint cartilage abnormalities or signi cant maltracking of articular surfaces [18,37]. In turn, low spectral activity analyzed by summing spectral power of the VAG signal in two bands: 50-250 Hz (P1) and 250-450 Hz (P2) seems to be related with intact integrity of the super cial layer of hyaline cartilage and an e cient lubrication mechanism [36,44].…”

Section: Discussionmentioning

confidence: 99%

“…The short-time spectra were obtained by computing the discrete Fourier transform of segments, 150 samples each, Hanning window, and 100 samples overlap of each segment. The spectral activity was analyzed by summing spectral power of the VAG signal in two bands: 50-250 Hz (P1) and 250-450 Hz (P2) [18,[36][37][38][39]. The performed ANOVA revealed that there was a signi cant main effect of the applied load on values of each analyzed parameter, similar in both lower limbs.…”

Section: Assessment Of Arthrokinematic Motion Qualitymentioning

confidence: 99%

“…It has been previously shown that VAG possess not only high accuracy and speci city when differentiating synovial joints' deteriorations with various biomechanical and morphological origins, but also is sensitive for identifying the changes in arthrokinematics related to the level of the joint load at the end of a performed task [16][17][18][19][20]. From an arthrology perspective, this nding seems to be particularly important, because the load on articular surfaces is one of the most essential factors affecting the level of kinetic friction and joint wear [7,21].…”

Section: Introductionmentioning

confidence: 99%

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Vibroarthrographic analysis of patellofemoral joint arthrokinematics during squats with increasing external loads

Ołowiana

Selkow

Laudner

et al. 2020

Preprint

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Background: The patellofemoral joint (PFJ) provides extremely low kinetic friction, which results in optimal arthrokinematic motion quality. Previous research showed that these friction-reducing properties may be diminished due to the increase in articular contact forces. However, this phenomenon has not been analyzed in vivo during functional daily-living activities. The aim of this study was the vibroarthrographic assessment of changes in PFJ arthrokinematics during squats with variated loads.Methods: 114 knees from 57 asymptomatic subjects (23 females and 34 males) whose ages ranged from 19 to 26 years were enrolled in this study. Participants were asked to perform 3 trials: 4 repetitions of bodyweight squats (L0), 4 repetitions of 10 kg barbell back loaded squats (L10), 4 repetitions of 20 kg barbell back loaded squats (L20). During the unloaded and loaded (L10, L20) squats, vibroarthrographic signals were collected using an accelerometer placed on the patella and were described by the following parameters: variation of mean square (VMS), mean range (R4), and power spectral density for frequency of 50-250 Hz (P1) and 250-450 Hz (P2).Results: Obtained results showed that the lowest values were noted in the unloaded condition and that the increased applied loads had a significant concomitant increase in all the aforementioned parameters bilaterally (p<0.05).Conclusion: This phenomenon indicates that the application of increasing knee loads during squats corresponds to higher intensity of vibroacoustic emission, which might be related to higher contact stress and kinetic friction as well as diminished arthrokinematic motion quality.

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Section: Discussionmentioning

confidence: 99%

Section: Assessment Of Arthrokinematic Motion Qualitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Vibroarthrographic analysis of patellofemoral joint arthrokinematics during squats with increasing external loads

Ołowiana

Selkow

Laudner

et al. 2020

Preprint

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“…On the other hand, the availability of modern imaging methods such as magnetic resonance imaging is limited due to high expense. One of the experimental methods developed for sensitive assessment of articular function is vibroarthrography (VAG) [ 1 , 2 , 3 ]. This relatively cheap and potentially widely available non-invasive method is based on the analysis of high frequency vibroacoustic emission, which is a natural phenomenon acquired from the relative motion of articular surfaces of the synovial joint.…”

Section: Introductionmentioning

confidence: 99%

“…Although frequency analysis is present in literature devoted to vibroarthrography, the state-of-the-art spectral features are designed to distinguish all classes at once [ 1 , 2 , 3 , 4 , 5 , 6 ], i.e., to utilize a single value to differentiate between specific conditions. The objective of this study was to find better frequency ranges, focusing on the distinction between each particular class pairs (i.e., ctrl-cmp1, ctrl-cmp2, ctrl-cmp3, …, cmp3-oa).…”

Section: Introductionmentioning

confidence: 99%

Vibroarthrographic Signal Spectral Features in 5-Class Knee Joint Classification

Łysiak

Fron

Bączkowicz

et al. 2020

Sensors

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Vibroarthrography (VAG) is a non-invasive and potentially widely available method supporting the joint diagnosis process. This research was conducted using VAG signals classified to five different condition classes: three stages of chondromalacia patellae, osteoarthritis, and control group (healthy knee joint). Ten new spectral features were proposed, distinguishing not only neighboring classes, but every class combination. Additionally, Frequency Range Maps were proposed as the frequency feature extraction visualization method. The results were compared to state-of-the-art frequency features using the Bhattacharyya coefficient and the set of ten different classification algorithms. All methods evaluating proposed features indicated the superiority of the new features compared to the state-of-the-art. In terms of Bhattacharyya coefficient, newly proposed features proved to be over 25% better, and the classification accuracy was on average 9% better.

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An Artificial Intelligent Methodology to Classify Knee Joint Disorder Using Machine Learning and Image Processing Techniques

Sharmila Begum,

Aruna,

Balajee

et al. 2024

Cognitive Analytics and Reinforcement Learning

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Analysis and multiclass classification of pathological knee joints using vibroarthrographic signals

Cited by 40 publications

References 25 publications

Vibroarthrographic analysis of patellofemoral joint arthrokinematics during squats with increasing external loads

Vibroarthrographic analysis of patellofemoral joint arthrokinematics during squats with increasing external loads

Vibroarthrographic Signal Spectral Features in 5-Class Knee Joint Classification

An Artificial Intelligent Methodology to Classify Knee Joint Disorder Using Machine Learning and Image Processing Techniques

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