Vibroarthrographic signals for the low-cost and computationally efficient classification of aging and healthy knees

Gong, Rui; Ohtsu, Hajime; Hase, Kazunori; Ota, Susumu

doi:10.1016/j.bspc.2021.103003

Cited by 10 publications

(4 citation statements)

References 31 publications

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“…For the VAG signals, the PRS features-sets showed a maximum accuracy of 93%, which is higher than that reported in previous studies [3,24]. These results validate the findings of Rauber et al [14] in 2015 that only appropriate features could improve the performance of the classifiers.…”

Section: Discussionsupporting

confidence: 85%

“…Electrocardiograms (ECGs) are commonly used to diagnose heart-related diseases, and electroencephalograms (EEGs) are used to determine sleep quality or mental state [1]. Electrical-based biomedical signals perform well, and magnetic, vibration, and acoustic-based biomedical signals are popularly used in clinical applications [2][3]. However, with increasingly shorter sampling intervals and longer signal lengths, analyzing 1D biomedical signals without the assistance of a computer has become a research limitation.…”

Section: Introductionmentioning

confidence: 99%

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Swarm Intelligence Algorithm Based on Plant Root System in 1D Biomedical Signal Feature Engineering to Improve Classification Accuracy

Gong¹,

Hase²

2023

Adv. technol. innov.

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The classification accuracy of one-dimensional (1D) biomedical signals is limited due to the lack of independence of the extracted features. To address this shortcoming, the study applies a swarm intelligence algorithm based on plant root systems (PRSs) to feature engineering. Some basic features of 1D biomedical signals are integrated into a digitized soil, and a root matrix is generated from this digitized soil and the PRS algorithm. The PRS features are extracted from the root matrix and used to classify the basic features. Following classification with the same biomedical signals and classifier, the accuracy of the added PRS set is generally higher than that of the base set. The result shows that the proposed algorithm can expand the application of 1D biomedical signals to include more biomedical signals in classification tasks for clinical diagnosis.

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Section: Discussionsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Swarm Intelligence Algorithm Based on Plant Root System in 1D Biomedical Signal Feature Engineering to Improve Classification Accuracy

Gong¹,

Hase²

2023

Adv. technol. innov.

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“…Exemplary features could include previously used measures in knee-joint VAG analysis. Those include time-domain features, such as rectified average value, root mean square value, and shape factors [ 55 , 56 ], and frequency-domain features, such as spectral entropy [ 57 ]. Additional preprocessing such as (ensemble) empirical mode decomposition [ 58 , 59 ] could also be studied in the context of TMJ.…”

Section: Discussionmentioning

confidence: 99%

Repeatability of the Vibroarthrogram in the Temporomandibular Joints

Łysiak

Marciniak

Bączkowicz

2022

Sensors

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Current research concerning the repeatability of the joint’s sounds examination in the temporomandibular joints (TMJ) is inconclusive; thus, the aim of this study was to investigate the repeatability of the specific features of the vibroarthrogram (VAG) in the TMJ using accelerometers. The joint sounds of both TMJs were measured with VAG accelerometers in two groups, study and control, each consisting of 47 participants (n = 94). Two VAG recording sessions consisted of 10 jaw open/close cycles guided by a metronome. The intraclass correlation coefficient (ICC) was calculated for seven VAG signal features. Additionally, a k-nearest-neighbors (KNN) classifier was defined and compared with a state-of-the-art method (joint vibration analysis (JVA) decision tree). ICC indicated excellent (for the integral below 300 Hz feature), good (total integral, integral above 300 Hz, and median frequency features), moderate (integral below to integral above 300 Hz ratio feature) and poor (peak amplitude feature) reliability. The accuracy scores for the KNN classifier (up to 0.81) were higher than those for the JVA decision tree (up to 0.60). The results of this study could open up a new field of research focused on the features of the vibroarthrogram in the context of the TMJ, further improving the diagnosing process.

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“…So far, a number of studies have been carried out in the field on knee VAG signal identification and analysis [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. These studies show that, the use of accelerometers was preferred to stethoscopes and microphones because of their insufficient low frequency responses in defining knee related problems.…”

Section: Introductionmentioning

confidence: 99%

Biyomedikal Uygulamalar için Çok Noktalı Çok Boyutlu İvmeölçer Veri Kayıt Sistemi

Akkan

Şenol

ÖZGÖREN

2022

Deu Muhendislik Fakultesi Fen Ve Muhendislik

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Bazı biyomedikal sinyal işleme uygulamaları, özel veri kaydı donanımı gerektirir. Diz ile ilgili non-invaziv teşhis ve çene ile ilgili elektroensefalografi (EEG) bozunum temizleme uygulamaları, eşzamanlı çok kanallı titreşim veri kaydı gerektiren iyi adaylar olacaktır. Bu çalışmada, yeni bir çok noktalı çok boyutlu ivme veri kayıt sistemi önerilmiştir. Bu sistem ivmeölçerler kullanarak aynı anda üç farklı noktadan üç boyutlu eğilim ve titreşim verilerini toplamaktadır. Çok kanallı sinyal analizi, sensör verilerini filtrelemek ve bileşenlerine ayırmak için eşzamanlı veri kayıtları gerektirir. Seçilen ivmeölçer, aynı anda üç eksen veri kaydı gerekliliğini sağlamaktadır. İvmeölçer veri kayıt sistemi, teşhis desteği için dizden ve EEG çene bozunum temizleme desteği için çeneden eğilim ve titreşim verileri elde etmek için kullanılabilir. Diz hareketlerinin titreşimlerini (vibroartrografik (VAG) sinyaller) tespit etmek için diz kapağı ve yan pozisyonlara üç ivmeölçer yerleştirilebilir. Elde edilen VAG sinyalleri, istatiksel veya zaman-frekans analiz teknikleri ile değerlendirilebilir. Ayrıca çene ve boyun hareketlerini kaydetmek için yüze üç adet ivmeölçer yerleştirilmiştir. Eş zamanlı olarak kaydedilen EEG ve çene verileri, istenmeyen boyun ve çene bozunumlarını çıkarmak için filtreleme veya istatistik yöntemler ile daha ileri bir şekilde analiz edilebilir.

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Vibroarthrographic signals for the low-cost and computationally efficient classification of aging and healthy knees

Cited by 10 publications

References 31 publications

Swarm Intelligence Algorithm Based on Plant Root System in 1D Biomedical Signal Feature Engineering to Improve Classification Accuracy

Swarm Intelligence Algorithm Based on Plant Root System in 1D Biomedical Signal Feature Engineering to Improve Classification Accuracy

Repeatability of the Vibroarthrogram in the Temporomandibular Joints

Biyomedikal Uygulamalar için Çok Noktalı Çok Boyutlu İvmeölçer Veri Kayıt Sistemi

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