Application of Acoustic Signal Processing Methods in Detecting Differences Between Open and Closed Kinematic Chain Movement for the Knee Joint

Karpiński, Robert; Machrowska, Anna; Maciejewski, Marcin

doi:10.35784/acs-2019-03

Cited by 13 publications

(6 citation statements)

References 26 publications

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“…These parameters were chosen on the basis of literature data and previous studies by the authors, who selected the optimal signal features. Based on the results of preliminary research and literature analysis, eight indicators were determined for the recorded acoustic signals (Karpiński, 2022;Karpiński et al, 2019;Karpiński et al, 2022a;Karpiński et al, 2022b):…”

Section: Feature Extractionmentioning

confidence: 99%

Comparison of Selected Classification Methods Based on Machine Learning as a Diagnostic Tool for Knee Joint Cartilage Damage Based on Generated Vibroacoustic Processes

KARPIŃSKI,

KRAKOWSKI,

JONAK

et al. 2023

Appl. Comput. Sci.

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Osteoarthritis is one of the most common cause of disability among elderly. It can affect every joint in human body, however, it is most prevalent in hip, knee, and hand joints. Early diagnosis of cartilage lesions is essential for fast and accurate treatment, which can prolong joint function. Available diagnostic methods include conventional X-ray, ultrasound and magnetic resonance imaging. However, those diagnostic modalities are not suitable for screening purposes. Vibroarthrography is proposed in literature as a screening method for cartilage lesions. However, exact method of signal acquisition as well as classification method is still not well established in literature. In this study, 84 patients were assessed, of whom 40 were in the control group and 44 in the study group. Cartilage status in the study group was evaluated during surgical treatment. Multilayer perceptron - MLP, radial basis function - RBF, support vector method - SVM and naive classifier – NBC were introduced in this study as classification protocols. Highest accuracy (0.893) was found when MLP was introduced, also RBF classification showed high sensitivity (0.822) and specificity (0.821). On the other hand, NBC showed lowest diagnostic accuracy reaching 0.702. In conclusion vibroarthrography presents a promising diagnostic modality for cartilage evaluation in clinical setting with the use of MLP and RBF classification methods.

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Section: Feature Extractionmentioning

confidence: 99%

Comparison of Selected Classification Methods Based on Machine Learning as a Diagnostic Tool for Knee Joint Cartilage Damage Based on Generated Vibroacoustic Processes

KARPIŃSKI,

KRAKOWSKI,

JONAK

et al. 2023

Appl. Comput. Sci.

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“…The sampling frequency was 1400 Hz and the resolution was 10 bits. Details of the measurement system are described in previous work (Karpiński, Machrowska & Maciejewski, 2019;Karpiński et al, 2022b). Signals were recorded for a closed kinetic chain (CKC) where standing up from a sitting position to full upright was realized.…”

Section: Participants and Study Protocolmentioning

confidence: 99%

“…The same is true for the knee joint and other joints in the human body. Changes in the mechanical properties and surface structure of the articular cartilage, such as the appearance of bumps, cracks or cartilage defects in the successive stages of degenerative changes, influence the vibroacoustic signals recorded during movement of the knee joint (Karpiński, Machrowska & Maciejewski, 2019;Prior et al, 2010;van den Borne et al, 2007). Fig.…”

Section: Introductionmentioning

confidence: 99%

Knee Joint Osteoarthritis Diagnosis Based on Selected Acoustic Signal Discriminants Using Machine Learning

Karpiński

2022

acs

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This paper presents the results of a preliminary study on simplified diagnosis of osteoarthritis of the knee joint based on generated vibroacoustic processes. The analysis was based on acoustic signals recorded in a group of 50 people, half of whom were healthy, and the other half - people with previously confirmed degenerative changes. Selected discriminants of the signals were determined and statistical analysis was performed to allow selection of optimal discriminants used at a later stage as input to the classifier. The best results of classification using artificial neural networks (ANN) of RBF (Radial Basis Function) and MLP (Multilevel Perceptron) types are presented. For the problem involving the classification of cases into one of two groups HC (Healthy Control) and OA (Osteoarthritis) an accuracy of 0.9 was obtained, with a sensitivity of 0.885 and a specificity of 0.917. It is shown that vibroacoustic diagnostics has great potential in the non-invasive assessment of damage to joint structures of the knee.

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“…Not all procedures are safe to be performed remotely by the patients or their family due to its invasiveness. For safety reasons, it is recommended to use mostly non invasive devices to measure parameters potentially crucial to patient condition assessment (Karpiński, Machrowska & Maciejewski, 2019;Machrowska, Karpiński, Krakowski & Jonak, 2019;Maciejewski et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Electrocardiogram Generation Software for Testing of Parameter Extraction Algorithms

Maciejewski

MACIEJEWSKA²,

Karpiński³

et al. 2020

acs

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Fast and automated ECG diagnosis is of great benefit for treatment of cardiovascular and other conditions. The algorithms used to extract parameters need to be precise, robust and efficient. Appropriate training and testing methods for such algorithms need to be implemented for optimal results. This paper presents a software solution for computer ECG generation and a simplified concept of testing process. All the parameters of the resulting generated signal can be tweaked and set properly. Such software can also be beneficial for training and educational use.

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Application of Acoustic Signal Processing Methods in Detecting Differences Between Open and Closed Kinematic Chain Movement for the Knee Joint

Cited by 13 publications

References 26 publications

Comparison of Selected Classification Methods Based on Machine Learning as a Diagnostic Tool for Knee Joint Cartilage Damage Based on Generated Vibroacoustic Processes

Comparison of Selected Classification Methods Based on Machine Learning as a Diagnostic Tool for Knee Joint Cartilage Damage Based on Generated Vibroacoustic Processes

Knee Joint Osteoarthritis Diagnosis Based on Selected Acoustic Signal Discriminants Using Machine Learning

Electrocardiogram Generation Software for Testing of Parameter Extraction Algorithms

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