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Malesevic, N.

Publications and source records attributed to Malesevic, N..

2 recordsLinked to original sources
bioRxiv2022

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

ObjectiveUltrasound 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. ApproachThis 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 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 resultsWe found that the spatial and temporal correlation between the MU-associated components from velBSS and stICA was high (0.86 {+/-} 0.05 and 0.87 {+/-} 0.06). The spike-triggered averaged twitch responses (using the MU spike trains from EMG) had an extremely high correlation (0.99 {+/-} 0.01). In addition, the computational time for velBSS was at least 50 times less than for stICA. SignificanceThe present algorithm (velBSS) outperforms the currently available method (stICA). 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.

bioengineering↗
bioRxiv2022

Comparing algorithms for assessing upper limb use with inertial measurement units

The various existing measures to quantify upper limb use from wrist-worn inertial measurement units (IMU) can be grouped into three categories: (a) Thresholded activity counting, (b) Gross movement score and (c) machine learning. While machine learning algorithms are a promising approach to detect upper limb use, there is currently no knowledge of the information used by these methods, and the data-related factors that influence their performance. A comparison of existing methods was carried out using data from a previous study which was collected from 10 unimpaired and 5 hemiparetic subjects, with annotation to identify periods of functional and non-functional upper limb use. Intra-subject random forest machine learning measures were found to classify upper limb use more accurately than other measures. The random forest measure uses information about the orientation and the amount of movement of the forearm to detect upper limb use. The types of movements and the proportion of functional data included in training/testing set influences the performance of machine learning measures. This study presents the first step towards understanding and optimizing machine learning methods for upper limb use assessment using wearable sensors.

bioengineering↗