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Can we diagnose disk and facet degeneration in lumbar spine by acoustic analysis of spine sounds?

A Correction to this article was published on 10 November 2020

This article has been updated

Abstract

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.

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Contributions

Concept development and idea for the research were contributed by AEC and EA. Design of the methods and experiments were performed by HT, MAA, and VN. Sound data collection, recording and processing were done by MAA, VN, and SA. Android APP development was done by MAA. MRIs were graded for disk (DD) and facet joint (FD) degeneration by VN and SA. Statistical data analysis and presentation of the results were performed by AEC. Literature search was done by HT, EA and AEC. Manuscript writing was performed by AEC, EA, and HT.

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Correspondence to Mustafa Arda Ahi.

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Nabi, V., Ayhan, S., Acaroglu, E. et al. Can we diagnose disk and facet degeneration in lumbar spine by acoustic analysis of spine sounds?. SIViP 15, 557–562 (2021). https://doi.org/10.1007/s11760-020-01776-3

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Keywords

  • Lower back pain (LBP)
  • Preventive spine healthcare
  • Spinal acoustic emission sounds
  • Artificial neural networks
  • Sound recognition
  • Mel-cepstrum