Synthesis of vibroarthrographic signals in knee osteoarthritis diagnosis training

• Published in: BMC research notes Vol. 9; no. 1; p. 352
• Main Authors: Shieh, Chin-Shiuh, Tseng, Chin-Dar, Chang, Li-Yun, Lin, Wei-Chun, Wu, Li-Fu, Wang, Hung-Yu, Chao, Pei-Ju, Chiu, Chien-Liang, Lee, Tsair-Fwu
• Format: Journal Article
• Language: English
• Published: London BioMed Central 19.07.2016; BioMed Central Ltd
• Subjects: Adult; Algorithms; Arthrometry, Articular - methods; Biomedical and Life Sciences; Biomedicine; Diagnosis; Elasticity Imaging Techniques - instrumentation; Elasticity Imaging Techniques - methods; Female; Fourier Analysis; Humans; Knee Joint - diagnostic imaging; Knee Joint - pathology; Life Sciences; Male; Medical students; Medicine/Public Health; Middle Aged; Musculoskeletal Disorders; Osteoarthritis; Osteoarthritis, Knee - diagnostic imaging; Osteoarthritis, Knee - pathology; Research Article; Signal Processing, Computer-Assisted; Stethoscopes; Training; Taiwan; Vibroarthrographic signals; Kellgren–Lawrence grading; Knee sound synthesis
• ISSN: 1756-0500; 1756-0500
• DOI: 10.1186/s13104-016-2156-6

Background Vibroarthrographic (VAG) signals are used as useful indicators of knee osteoarthritis (OA) status. The objective was to build a template database of knee crepitus sounds. Internships can practice in the template database to shorten the time of training for diagnosis of OA. Methods A knee sound signal was obtained using an innovative stethoscope device with a goniometer. Each knee sound signal was recorded with a Kellgren–Lawrence (KL) grade. The sound signal was segmented according to the goniometer data. The signal was Fourier transformed on the correlated frequency segment. An inverse Fourier transform was performed to obtain the time-domain signal. Haar wavelet transform was then done. The median and mean of the wavelet coefficients were chosen to inverse transform the synthesized signal in each KL category. The quality of the synthesized signal was assessed by a clinician. Results The sample signals were evaluated using different algorithms (median and mean). The accuracy rate of the median coefficient algorithm (93 %) was better than the mean coefficient algorithm (88 %) for cross-validation by a clinician using synthesis of VAG. Conclusions The artificial signal we synthesized has the potential to build a learning system for medical students, internships and para-medical personnel for the diagnosis of OA. Therefore, our method provides a feasible way to evaluate crepitus sounds that may assist in the diagnosis of knee OA.