University of Twente Student Theses


Estimate motor unit organization in Nociceptive Flexion Reflex using Convolutive Blindsource Separation and statistical signal analysis

Duong, X.L. (2022) Estimate motor unit organization in Nociceptive Flexion Reflex using Convolutive Blindsource Separation and statistical signal analysis.

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Abstract:Although chronic pain has shown a great adversity to human health, the mechanism of this illness is still not fully understood. Nociceptive flexion reflex (NFR), a physiological withdraw response after cutaneous stimulation, has been widely used as a method to comprehend pain modulation. This reflex is believed to be a suitable tool to diagnosis early development of chronic pain. Thus, our study has a significant focus on NFR, especially at single motor unit (MU) level. The purpose of our study is to establish an analysis framework to investigate MUs’ activity in NFR using the high density surface electromyogram (HDsEMG) grid electrode. The method idea is to elicit the NFR response by electrically stimulating at the sural nerve pathway on subjects’ ankle, while the bicep femoris EMG activity signal is recorded, following the Multiple Threshold Tracking (MTT) protocol. The recorded results then are analyzed with the Convolutive Blindsource Separation (BSS) decomposition algorithm in the offline analysis process. In total, there are 11 subjects attended in our experiment. Within them, only 6 subjects (54.5%) exhibit the NFR responses with our protocol. 2 participants (subject 9 and 10) have sufficient reflex-perceived stimuli to estimate the NDT level (9.92mA) and can continue to the offline data analysis. For the decomposition process, we found out that Convolutive BSS algorithm is optimal with 30-second batches, 150 iterations and with an adaptive method of selecting peaks. We also confirm that the MUs returned has an acceptable quality, with the outcome SIL estimated as 84.82% for total 679 MUs found. Besides that, we develop a method to quality control selecting merged MUs using multi-segment CoVs. This method shows flexibility and validity in optimizing the number of MUs. However, through this study, it is also observed that the Convolutive BSS decomposition do not perform well with signals containing strong NFRs, and greatly depends on the subject continuous contraction level. This leads to a small number of MUs, and multiple MUs do not have significant length due to difficulty in merging process. For the final result, a small observation is noted that subject 9’s MUs shows the following of the Henneman’s size principle of recruitment, where slower smaller fibers are admitted before the bigger fiber against magnitude increasing stimulation. Further study with larger dataset need to be used to confirm this observation.
Item Type:Essay (Master)
Faculty:TNW: Science and Technology
Subject:02 science and culture in general, 44 medicine, 50 technical science in general, 54 computer science
Programme:Biomedical Engineering MSc (66226)
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