A fast blind source separation algorithm for decomposing ultrafast ultrasound images into spatiotemporal muscle unit kinematics

• Published in: Journal of neural engineering Vol. 20; no. 3; pp. 34001 - 34009
• Main Authors: Rohlén, Robin, Lundsberg, Jonathan, Malesevic, Nebojsa, Antfolk, Christian
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.06.2023
• Subjects: Algorithms; Basic Medicine; Biomechanical Phenomena; blind source separation; decomposition; Electromyography - methods; fast algorithm; Fysiologi; Fysiologi och anatomi; Humans; Isometric Contraction - physiology; Medical and Health Sciences; Medicin och hälsovetenskap; Medicinska och farmaceutiska grundvetenskaper; motor unit; Muscle Contraction - physiology; Muscle, Skeletal - diagnostic imaging; Muscle, Skeletal - physiology; Physiology; Physiology and Anatomy; ultrasound; motor unit; decomposition; fast algorithm; blind source separation; ultrasound
• ISSN: 1741-2560; 1741-2552; 1741-2552
• DOI: 10.1088/1741-2552/acd4e9

Objective. Ultrasound can detect individual motor unit (MU) activity during voluntary isometric contractions based on their subtle axial displacements. The detection pipeline, currently performed offline, is based on displacement velocity images and identifying the subtle axial displacements. This identification can preferably be made through a blind source separation (BSS) algorithm with the feasibility of translating the pipeline from offline to online . However, the question remains how to reduce the computational time for the BSS algorithm, which includes demixing tissue velocities from many different sources, e.g. the active MU displacements, arterial pulsations, bones, connective tissue, and noise. Approach. This study proposes a fast velocity-based BSS (velBSS) algorithm suitable for online purposes that decomposes velocity images from low-force voluntary isometric contractions into spatiotemporal components associated with single MU activities. The proposed algorithm will be compared against spatiotemporal independent component analysis (stICA), i.e. the method used in previous papers, for various subjects, ultrasound- and EMG systems, where the latter acts as MU reference recordings. Main results . We found that the computational time for velBSS was at least 20 times less than for stICA, while the twitch responses and spatial maps extracted from stICA and velBSS for the same MU reference were highly correlated (0.96 ± 0.05 and 0.81 ± 0.13). Significance. The present algorithm (velBSS) is computationally much faster than the currently available method (stICA) while maintaining the same performance. It provides a promising translation towards an online pipeline and will be important in the continued development of this research field of functional neuromuscular imaging.