Crepitus vibrational and acoustic signal analysis of the human facet joints of the lumbar spine has historically been a difficult problem due to the inhomogeneous and varied signal characteristics. Here we improve upon our previous automated computational method, now enhancing it for analysis of human crepitus. Compared with this group's previous studies using a mechanical model; human crepitus is extremely complex. Moreover, there is no existing availability of large numbers of human crepitus data to enable effective machine learning approaches. Therefore, we proposed an automated method (AM) of analysis that, analogous to machine learning, used a test set (n = 16) and an experimental set of data (n = 48). The advantage of beginning with this approach was that we identified characteristics of the signal that are unavailable or otherwise not easily obtained in more advanced methods, such as “black box” machine learning methods. However, we did not have the high fidelity that a machine learning approach would provide. This was shown by only a fair level of inter-rater agreement (Kw = 0.367; SE = 0.054, 95% CI = 0.260-0.474) between the AM and human observers before adjustments were made in the AM. Following adjustments to the AM, inter-rater agreement improved to a substantial level of agreement (Kw = 0.788; SE = 0.056, 95% CI = 0.0.682-0.895). In the future, we recommend a machine learning study with a high number of subjects, that can better capture the nuances of varying types of human crepitus.
Objective This feasibility study used novel accelerometry (vibration) and microphone (sound) methods to assess crepitus originating from the lumbar spine before and after side-posture spinal manipulation (SMT). Methods This study included 5 healthy and 5 low back pain (LBP) subjects. Nine accelerometers and 1 specialized directional microphone were applied to the lumbar region, allowing assessment of crepitus. Each subject underwent full lumbar ranges of motion (ROM), bilateral lumbar SMT, and repeated full ROM. Following full ROMs the subjects received side-posture lumbar SMT on both sides by a licensed doctor of chiropractic. Accelerometer and microphone recordings were made during all pre- and post-SMT ROMs. Primary outcome was a descriptive report of crepitus prevalence (average number of crepitus events/subject). Subjects were also divided into 3 age groups for comparisons (18–25, 26–45, and 46–65 years). Results Overall, crepitus prevalence decreased pre-post SMT (average pre= 1.4 crepitus/subject vs. post= 0.9). Prevalence progressively increased from the youngest to oldest age groups (pre-SMT= 0.0, 1.67, and 2.0, respectively; and post-SMT= 0.5, 0.83, and 1.5). Prevalence was higher in LBP subjects compared to healthy (pre-SMT-LBP= 2.0, vs. pre-SMT-healthy= 0.8; post-SMT-LBP= 1.0 vs. post-SMT-healthy= 0.8), even though healthy subjects were older than LBP subjects (40.8 years vs. 27.8 years); accounting for age: pre-SMT-LBP= 2.0 vs. pre-SMT-healthy= 0.0; post-SMT-LBP= 1.0 vs. post-SMT-healthy= 0.3. Conclusions Our findings showed that a larger study is feasible. Other findings included that crepitus prevalence increased with age, was higher in LBP than healthy subjects, and overall decreased following SMT. This study showed that crepitus assessment using accelerometers has the potential of being an outcome measure/biomarker for assessing spinal joint (facet/Z joint) function during movement and the effects of LBP treatments (eg, SMT) on Z joint function.
The vibration and acoustic emissions produced within facet joints of the lumbar spine, known as crepitus, can be a potential biomarker to identify decreased joint functioning and the site of low back pain. Using piezoelectric accelerometers and a silicone "phantom" mechanical model we sought to identify the site of crepitus. Past analyses of these data with human observers have been too time consuming for eventual practical clinical application, and a more expedient algorithmic method of analysis is preferable. In this study the signal filtering and processing functions of MATLAB were harnessed to filter aberrant noise as well as determine the location (level and left or right side) from which crepitus originated during induced crepitus events in the phantom model (n=30). Development of this automated method refined the definition of facet joint crepitus. The automated method was found to be as reliable and valid as assessment by human observers and took significantly less time (p=0.009). Future studies will assess the reliability of the automated method to detect this phenomenon in humans.
The vibration and acoustic emissions produced within facet joints of the lumbar spine, known as crepitus, can be a potential biomarker to identify decreased joint functioning and the site of low back pain. Using piezoelectric accelerometers and a silicone “phantom” mechanical model we sought to identify the site of crepitus. Past analyses of these data with human observers have been too time consuming for eventual practical clinical application, and a more expedient algorithmic method of analysis is preferable. In this study the signal filtering and processing functions of MATLAB were harnessed to filter aberrant noise as well as determine the location (level and left or right side) from which crepitus originated during induced crepitus events in the phantom model (n = 30). Development of this automated method refined the definition of facet joint crepitus. The automated method was found to be as reliable and valid as assessment by human observers, and took significantly less time (p = 0.009). Future studies will assess the reliability of the automated method to detect this phenomenon in humans.
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