The new descriptor in processing of vibroacoustic signal of knee joint

Łysiak, Adam; Fron, A.; Bączkowicz, Dawid; Szmajda, M.

doi:10.1016/j.ifacol.2019.12.683

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…Using the spatiotemporal analysis method Wu recommends to use adaptive segmentation [ 8 , 9 ] to avoid redundancy in given segments, and emphasizes its usefulness for calculating sets of features and signal classification. The statistical analysis is often used to determine distribution measures and statistical parameters [ 10 , 11 ], giving the possibility to display data in the form of a tabular summary [ 12 ] and graphic forms such as the histogram [ 6 ] or the box plot [ 13 , 14 , 15 ], and to analyze variations using various types of tests (t-test [ 16 ], Kruskal–Wallis test [ 13 ], one-way ANOVA test [ 4 ], Wilcoxon rank-sum test [ 11 ]). The time-frequency analysis and the frequency analysis supported by using statistical analysis are commonly used in the field of signal processing.…”

Section: Standard Descriptorsmentioning

confidence: 99%

“…The frequently used VAG signal analysis is the short-time Fourier transform (STFT) extended by the visual representation of a spectrogram. Łysiak et al [ 14 ] used STFT and spectrograms, on whose specific analysis the new three descriptors have been proposed. Dołęgowski et al [ 15 ] proposed the incremental decomposition of voltage in time and the spectrogram as the methods to identify the knee joint disease stage and compared the statistical parameters of normalized energy values of the band 50–250 Hz (P1) and 250–450 Hz (P2).…”

Section: Standard Descriptorsmentioning

confidence: 99%

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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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Section: Standard Descriptorsmentioning

confidence: 99%

Section: Standard Descriptorsmentioning

confidence: 99%

Vibroarthrographic Signal Spectral Features in 5-Class Knee Joint Classification

Łysiak

Fron

Bączkowicz

et al. 2020

Sensors

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“…Despite the VAG method being still in development, it already shows very promising accuracy, sensitivity and specificity in joints affected with various disorders compared to the healthy ones [ 28 , 33 , 43 , 44 ]. As this method is relatively new, there are no strict requirements for using measuring equipment or for conducting examinations [ 28 , 45 ]. Moreover, the waveforms of VAG signals are characterised by non-stationarity and, in the above-mentioned studies, linear parameters were the basis for analysis.…”

Section: Introductionmentioning

confidence: 99%

Using Nonlinear Vibroartrographic Parameters for Age-Related Changes Assessment in Knee Arthrokinematics

Kręcisz

Bączkowicz

Kawala-Sterniuk

2022

Sensors

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Changes in articular surfaces can be associated with the aging process and as such may lead to quantitative and qualitative impairment of joint motion. This study is aiming to evaluate the age-related quality of the knee joint arthrokinematic motion using nonlinear parameters of the vibroarthrographic (VAG) signal. To analyse the age-related quality of the patellofemoral joint (PFJ), motion vibroarthrography was used. The data that were subject to analysis represent 220 participants divided into five age groups. The VAG signals were acquired during flexion/extension knee motion and described with the following nonlinear parameters: recurrence rate (RR) and multi-scale entropy (MSE). RR and MSE decrease almost in a linear way with age (main effects of group p<0.001; means (SD): RR=0.101(0.057)−0.020(0.017); and MSE=20.9(8.56)−13.6(6.24)). The RR post-hoc analysis showed that there were statistically significant differences (p<0.01) in all comparisons with the exception of the 5th–6th life decade. For MSE, statistically significant differences (p<0.01) occurred for: 3rd–7th, 4th–7th, 5th–7th and 6th life decades. Our results imply that degenerative age-related changes are associated with lower repeatability, greater heterogeneity in state space dynamics, and greater regularity in the time domain of VAG signal. In comparison with linear VAG measures, our results provide additional information about the nature of changes of the vibration dynamics of PFJ motion with age.

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“…The measurement science is essential for the progress in the such conventional areas as: science and technologies [1][2][3], ecology [4], medicine [5][6][7], but recently also finds application in economy [8] forensics [9], cybersecurity [10]. A new trend in metrology is the use of robust approaches and machine learning technologies to improve metrological characteristics of measuring tools, including increased accuracy of measurements [11,12].…”

Section: Introductionmentioning

confidence: 99%

Metrology and Measurement Systems

Jun,

Szmajda,

Khoma

et al. 2020

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The aim of this paper is to compare the efficiency of various outlier correction methods for ECG signal processing in biometric applications. The main idea is to correct anomalies in various segments of ECG waveform rather than skipping a corrupted ECG heartbeat in order to achieve better statistics. Experiments were performed using a self-collected Lviv Biometric Dataset. This database contains over 1400 records for 95 unique persons. The baseline identification accuracy without any correction is around 86%. After applying the outlier correction the results were improved up to 98% for autoencoder based algorithms and up to 97.1% for sliding Euclidean window. Adding outlier correction stage in the biometric identification process results in increased processing time (up to 20%), however, it is not critical in the most use-cases.

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The new descriptor in processing of vibroacoustic signal of knee joint

Cited by 7 publications

References 10 publications

Vibroarthrographic Signal Spectral Features in 5-Class Knee Joint Classification

Vibroarthrographic Signal Spectral Features in 5-Class Knee Joint Classification

Using Nonlinear Vibroartrographic Parameters for Age-Related Changes Assessment in Knee Arthrokinematics

Metrology and Measurement Systems

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