Detection method of flexion relaxation phenomenon based on wavelets for patients with low back pain Advances in Nonstationary Electrophysiological Signal Analysis and Processing

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Abstract

The flexion relaxation phenomenon (FRP) can be defined as a reduction or silence of myoelectric activity of the lumbar erector spinae muscle during full trunk flexion. It is typically absent in patients with chronic low back pain (LBP). Before any broad clinical utilization of this neuromuscular response can be made, effective, standardized, and accurate methods of identifying FRP limits are needed. However, this phenomenon is clearly more difficult to detect for LBP patients than for healthy patients. The main goal of this study is to develop an automated method based on wavelet transformation that would improve time point limits detection of surface electromyography signals of the FRP in case of LBP patients. Conventional visual identification and proposed automated methods of time point limits detection of relaxation phase were compared on experimental data using criteria of accuracy and repeatability based on physiological properties. The evaluation demonstrates that the use of wavelet transform (WT) yields better results than methods without wavelet decomposition. Furthermore, methods based on wavelet per packet transform are more effective than algorithms employing discrete WT. Compared to visual detection, in addition to demonstrating an obvious saving of time, the use of wavelet per packet transform improves the accuracy and repeatability in the detection of the FRP limits. These results clearly highlight the value of the proposed technique in identifying onset and offset of the flexion relaxation response in LBP subjects. © 2012 Nougarou et al.; licensee Springer.

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Nougarou, F., Massicotte, D., & Descarreaux, M. (2012). Detection method of flexion relaxation phenomenon based on wavelets for patients with low back pain Advances in Nonstationary Electrophysiological Signal Analysis and Processing. Eurasip Journal on Advances in Signal Processing, 2012(1). https://doi.org/10.1186/1687-6180-2012-151

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