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Adaptive signal processing techniques for analysis of knee joint vibroarthrographic signals.

机译:自适应信号处理技术,用于分析膝关节颤动心电图信号。

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Knee joint articular cartilage diseases such as arthritis affect a significant portion of the elderly. Early noninvasive diagnosis can prevent trauma to the patient, avoid surgery, and reduce health care expenditures. Difficulties exist with imaging and arthroscopy in noninvasively evaluating cartilage surfaces. An interesting possibility of assessing cartilage surface noninvasively is to analyze the vibrations or vibroarthrographic (VAG) signals emitted during normal leg movement.; This thesis explores the diagnostic potential of VAG signals in screening abnormal knees from normal knees via adaptive signal processing techniques. VAG signal characteristics pose severe challenges in terms of analysis and extraction of discriminant features. One of the main characteristics of VAG signals is nonstationarity, which is addressed in the thesis by the use of segmentation-based techniques and time-frequency distributions (TFDs). TFDs overcome the difficulties with segmentation-based methods in relating clinical information with VAG signals.; Novel approaches for denoising VAG signals and construction of adaptive TFDs based on signal decomposition are proposed. The proposed methods are tested with synthetic signals before applying them to real VAG signals. Pattern classification of VAG signal features indicate accuracy up to 86% in screening abnormal knees from normal knees, and has particularly shown high sensitivity in screening patellofemoral articular cartilage disorders such as chondromalacia patella.; An objective method for identifying features in TFDs using an image process' technique in the form of the Hough-Radon transform (HRT) is proposed. The HRT method is tested on TFDs with known time-frequency (TF) dynamics, and is shown to have good potential in automatically identifying TF signatures.; Direct auscultation of knee joints has been a traditional mode of diagnosis. The thesis proposes a computer-aided auscultation technique using auditory display procedures. Subjective evaluation of VAG signals with the proposed sonification technique based on the instantaneous mean frequency has indicated a sensitivity of 83% in screening abnormal knees, although at the expense of a decrease in specificity.; The techniques proposed in the thesis are being incorporated in a diagnostic workstation being prototyped.
机译:诸如关节炎之类的膝关节软骨疾病影响了老年人的很大一部分。早期的非侵入性诊断可以防止对患者造成创伤,避免手术并减少医疗保健支出。在非侵入性评估软骨表面时,成像和关节镜检查存在困难。无创地评估软骨表面的一种有趣的可能性是分析正常腿部运动过程中发出的振动或振动波谱(VAG)信号。本文探讨了通过自适应信号处理技术从正常膝关节筛查异常膝关节中VAG信号的诊断潜力。 VAG信号特性在鉴别特征的分析和提取方面提出了严峻的挑战。 VAG信号的主要特征之一是非平稳性,本文通过使用基于分段的技术和时频分布(TFD)解决了这一问题。 TFD克服了基于分段的方法将临床信息与VAG信号相关联的困难。提出了一种新的VAG信号去噪方法和基于信号分解的自适应TFD构造方法。在将合成信号应用于实际VAG信号之前,先对它们进行了测试。 VAG信号特征的模式分类表明从正常膝盖筛查异常膝盖的准确性高达86%,并且在筛查tell股关节软骨疾病(例如软骨软化骨)方面尤其显示出高敏感性。提出了一种使用图像处理技术以Hough-Radon变换(HRT)形式识别TFD中特征的客观方法。 HRT方法在具有已知时频(TF)动态特性的TFD上进行了测试,并显示出在自动识别TF签名方面的良好潜力。膝关节直接听诊一直是传统的诊断方法。本文提出了一种利用听觉显示程序的计算机辅助听诊技术。用所提出的基于瞬时平均频率的超声技术对VAG信号进行主观评估表明,在筛查异常膝盖时灵敏度为83%,尽管以降低特异性为代价。论文中提出的技术被并入到正在原型化的诊断工作站中。

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