Synthesis of vibroarthrographic signals in knee osteoarthritis diagnosis training

Shieh, Chin‐Shiuh; Tseng, Chin‐Dar; Chang, Liyun; Lin, Wei‐Chun; Wu, Li‐Fu; Wang, Hung‐Yu; Chao, Pei‐Ju; Chiu, Chien‐Liang; Lee, Tsair‐Fwu

doi:10.1186/s13104-016-2156-6

Cited by 14 publications

(6 citation statements)

References 33 publications

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“…Previous vibroarthrography-related research also performed in OKC showed that articular surfaces with chondral lesions possess diminished capabilities of friction reduction and generate a distinct VAG waveform pattern compared to those from healthy joints [12, 20, 21]. Accordingly, in our study, knees with chondromalacic changes were characterized by signals with higher amplitude and variation, when compared to the CTRS group.…”

Section: Discussionsupporting

confidence: 49%

“…It is believed that such analysis provides specific information about tribological properties of the joint, related among other factors to hyaline cartilage status and/or rheological characteristics of synovial fluid [18, 19]. Thus, the VAG method provides clinicians insight into mechanical, friction-reducing features of the articular environment, which reflects the overall condition of the joint, with special regard to its function [20]. This broadens the possibilities of physical examination and enables the selection of specific physical therapy interventions.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of patellofemoral arthrokinematic motion quality in open and closed kinetic chains using vibroarthrography

Bączkowicz

Kręcisz

Borysiuk

2019

BMC Musculoskelet Disord

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BackgroundKnee movements performed in open (OKC) and closed (CKC) kinetic chains generate various patterns of muscle activities and especially distinct contact stresses in the patellofemoral joint (PFJ). In contrast to these features, the arthrokinematic motion quality (AMQ) of the PFJ has not been compared between mentioned conditions. In this study we performed vibroarthrographic analysis of AMQ in movements performed in OKC and CKC, in healthy subjects and individuals with chondromalacia patellae, to assess which of the test conditions is more efficient in differentiation between healthy and deteriorated joints. Moreover, our analysis will broaden the knowledge related to behavior of normal and pathological synovial joints during motion with and without weight bearing. It is an essential issue, due to the recently observed significant interest in comparing potential benefits and limitations of CKC and OKC exercises as they relate to lower extremity rehabilitation.Methods100 subjects (62 healthy controls and 38 subjects with PFJ chondromalacia) were enrolled. During repeated knee flexion/extension motions performed in OKC (in a sitting position) and CKC (sit-to-stand movements), the vibroarthrographic signals were collected using an accelerometer and described by variability (VMS), amplitude (R4), and spectral power in 50–250 Hz (P1) and 250–450 Hz (P2) bands.ResultsSignificant differences in VMS [V], R4 [V], P1 [V2/Hz] and P2 [V2/Hz] between OKC and CKC were found (0.0001, 0.969. 0.800 0.041 vs 0.013, 3.973, 6.790, 0.768, respectively, P < 0.001). Moreover, in both analyzed load-related conditions the subjects with chondromalacia were characterized by significantly higher values of all parameters, when compared to controls (P < 0.001), with effect size values over 0.6.ConclusionsWe showed that motion of the physiological, unloaded PFJ articular surfaces in OKC is nearly vibrationless, which corresponds with optimal AMQ of PFJ, while loaded movements in CKC are characterized by a higher vibroacoustic emission level. Moreover, chondral lesions should be considered as an increased friction-related, aggravating factor of AMQ, which is critical in CKC movements under load. Nonetheless, OKC and CKC conditions are characterized by large effect sizes, and provide an efficient test frame for differentiating physiological knees and joints with chondral lesions.

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Section: Discussionsupporting

confidence: 49%

Section: Introductionmentioning

confidence: 99%

Analysis of patellofemoral arthrokinematic motion quality in open and closed kinetic chains using vibroarthrography

Bączkowicz

Kręcisz

Borysiuk

2019

BMC Musculoskelet Disord

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“…Kellgren-Lawrence (KL) grade system was developed in Shieh et al (2016) for recording the vibroarthrographic signals to identify knee Osteoarthritis. However, accurate knee osteoarthritis detection was not performed.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Machine learning based identification and classification of disorders in human knee joint – computational approach

Balajee

Venkatesan

2021

Soft Comput

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Earlier identification of knee joint pathology helps the therapist to provide the appropriate clinical procedures to control the deteriorating process of arthritis. Beyond usual medical investigations, computational techniques have been used for the diagnosis of knee joint disorder. Among different methodologies, Vibroarthrographic technique is employed to identify knee joint disorder. Machine Learning contains number of classification methods for the given data. A novel technique called Greedy sequential backward feature selection based Radial kernelized least square support vector classification (GSBFS-RKLSSVC) is introduced for accurate detection of knee joint pathology with minimum time. The proposed GSBFS-RKLSSVC technique consists of three processes namely feature selection, feature evaluation and classification. Initially number of VAG signal images are taken from the dataset for detection of knee joint disorder. The relevant feature is selected through the Greedy mutual informative regressed sequential backward selection algorithm to reduce an initial dimensional feature space into a low dimensional feature subspace. Following this the dichotomous logit regression is applied to select the best features and discard others. Therefore, the feature selection process of the proposed GSBFS-RKLSSVC minimizes the time consumption of the knee joint pathology detection. Once the signal features are extracted, RKLSSVC is applied to detect the normal and abnormal VAG signal. Decision boundary is utilized by the classifier to categorize the samples based on the similarity between the training features and testing features. As a result, the accurate classification is obtained with a minimum error rate. The observed result indicates that GSBFS-RKLSSVC achieves higher accuracy, sensitivity, specificity and reduces time than the conventional methods.

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“…In [32][33][34], researchers used sound vibration signals to evaluate the degree of recovery for patellofemoral pain syndrome, OA, and skeletal muscle damage, respectively. In the current study, the receiver positions of the collected signals were based on those used in [32][33][34] and the recommendations of a clinical orthopedic specialist. Figure 1a-c shows a schematic diagram of the measurement process, a photograph of the device, and a diagram of the flexion-to-extension swing measurement, respectively.…”

Section: Signal Capturingmentioning

confidence: 99%

“…Signals processed by a digital convertor in a microcontroller unit (Arduino Uno, Scarmagno, Italy) were transmitted to a computer for processing via a USB 2.0 port. the receiver positions of the collected signals were based on those used in [32][33][34] and the recommendations of a clinical orthopedic specialist. Figure 1a-c shows a schematic diagram of the measurement process, a photograph of the device, and a diagram of the flexion-to-extension swing measurement, respectively.…”

Section: Signal Capturingmentioning

confidence: 99%

Developmental Screening System for Patient Vibration Signals with Knee Disorder

Tuan

et al. 2019

Applied Sciences

Self Cite

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In this paper, we introduce a simple sound signal diagnostic method to evaluate anterior cruciate ligament (ACL) injury before and after reconstructive surgery. Sixty-five recruited participants were divided into control (n = 27) and experimental (n = 38) groups. Dynamic time warping of sound signals was applied to evaluate the healthy and injured limbs before surgery with those after surgery via analysis of variance and Z-test analysis. In the control group, the average differences among three sensing points ranged from 7.7 ± 3.4 to 18.9 ± 10.6, over the frequency range of 250 Hz to 4 kHz. In the experimental group, the average differences were between 6.2 ± 2.8 and 57.4 ± 21.3. The greatest significant wavelet coefficient difference was observed in the range of 125–250 Hz. Our preliminary results demonstrate that the proposed approach produces significant signal variations at the ACL test point (TPACL) with regard to identifying ACL injury, with swing cycles conducted within the 4-kHz band of 1–7 wavelet coefficients. Thus, wavelet analysis of knee sound can be used to evaluate the recovery status after single ACL reconstruction surgery. After a 1-year follow-up of the 38 patients with ACL injury, the frequency band difference among sensing points was reduced from 274% to approximately 600%.

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Synthesis of vibroarthrographic signals in knee osteoarthritis diagnosis training

Cited by 14 publications

References 33 publications

Analysis of patellofemoral arthrokinematic motion quality in open and closed kinetic chains using vibroarthrography

Analysis of patellofemoral arthrokinematic motion quality in open and closed kinetic chains using vibroarthrography

RETRACTED ARTICLE: Machine learning based identification and classification of disorders in human knee joint – computational approach

Developmental Screening System for Patient Vibration Signals with Knee Disorder

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