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

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,...

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Bibliographic Details
Published in:Signal, image and video processing image and video processing, 2021-04, Vol.15 (3), p.557-562
Main Authors: Nabi, Vugar, Ayhan, Selim, Acaroglu, Emre, Ahi, Mustafa Arda, Toreyin, Hakan, Cetin, A. Enis
Format: Article
Language:English
Subjects:
Acoustics
Algorithms
Artificial neural networks
Automatic speech recognition
Computer Imaging
Computer Science
Degeneration
Feature extraction
Image Processing and Computer Vision
Multimedia Information Systems
Original Paper
Pattern Recognition and Graphics
Signal,Image and Speech Processing
Sound
Vision
Voice recognition
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Summary: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.
ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-020-01776-3