Chin-Shiuh Shieh1,2, Chin-Dar Tseng1, Li-Yun Chang3, Wei-Chun Lin4,5, Li-Fu Wu1, Hung-Yu Wang1,2, Pei-Ju Chao1,6, Chien-Liang Chiu1, Tsair-Fwu Lee7,8. 1. Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Applied Sciences, 415, Chien Kung Road, San-Min District, Kaohsiung, 807, Taiwan, ROC. 2. Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan, ROC. 3. Department of Medical Imaging and Radiological Sciences, I-Shou University, Kaohsiung, 82445, Taiwan, ROC. 4. Institute of Photonics and Communications, National Kaohsiung University of Applied Sciences, Kaohsiung, 80778, Taiwan, ROC. 5. Department of Orthopedic, Kaohsiung Municipal Min-Sheng Hospital, Kaohsiung, 80276, Taiwan, ROC. 6. Department of Radiation Oncology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, 83342, Taiwan, ROC. 7. Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Applied Sciences, 415, Chien Kung Road, San-Min District, Kaohsiung, 807, Taiwan, ROC. tsairfwulee@gmail.com. 8. Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan, ROC. tsairfwulee@gmail.com.
Abstract
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.
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.
Authors: Jeffrey W Prescott; Michael Pennell; Thomas M Best; Mark S Swanson; Furqan Haq; Rebecca Jackson; Metin N Gurcan Journal: Conf Proc IEEE Eng Med Biol Soc Date: 2009
Authors: C G Peterfy; A Guermazi; S Zaim; P F J Tirman; Y Miaux; D White; M Kothari; Y Lu; K Fye; S Zhao; H K Genant Journal: Osteoarthritis Cartilage Date: 2004-03 Impact factor: 6.576