This study aims to investigate spine sounds from a perspective that would make their use for diagnostic purposes of any spinal pathology possible. People with spine problems can be determined using joint sounds collected from the involved area of the spinal columns of subjects. In our sound dataset, it is observed that a ‘click’ sound is detected in individuals who are suffering from low back pain. Recorded joint sounds are classified using automatic speech recognition algorithm. mel-frequency cepstrum coefficients (MFCC) are extracted from the sound signals as feature vectors. MFCC’s are classified using an artificial neural networks, which is currently the state-of-the-art speech recognition tool. The algorithm has a high success rate of detecting ‘click’ sounds in a given sound signal and it can perfectly identify and differentiate healthy individuals from unhealthy subjects in our data set. Spine sounds have the potential of serving as a reliable marker of the spine health.

中文翻译：

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我们可以通过脊柱声音的声学分析来诊断腰椎椎间盘和小关节退变吗？

本研究旨在从一个角度研究脊柱声音，使它们可用于任何脊柱病理的诊断目的。可以使用从受试者脊柱相关区域收集的关节声音来确定患有脊柱问题的人。在我们的声音数据集中，观察到在患有腰痛的个体中检测到“咔哒”声。使用自动语音识别算法对记录的关节声音进行分类。从声音信号中提取梅尔频率倒谱系数 (MFCC) 作为特征向量。MFCC 使用人工神经网络进行分类，这是目前最先进的语音识别工具。该算法在给定声音信号中检测“咔哒”声的成功率很高，并且可以完美地识别和区分我们数据集中的健康个体和不健康个体。脊柱声音有可能成为脊柱健康的可靠标志。