Decomposition of surface EMG signals from cyclic dynamic contractions.

Over the past 3 decades, various algorithms used to decompose the electromyographic (EMG) signal into its constituent motor unit action potentials (MUAPs) have been reported. All are limited to decomposing EMG signals from isometric contraction. In this report, we describe a successful approach to decomposing the surface EMG (sEMG) signal collected from cyclic (repeated concentric and eccentric) dynamic contractions during flexion/extension of the elbow and during gait. The increased signal complexity introduced by the changing shapes of the MUAPs due to relative movement of the electrodes and the lengthening/shortening of muscle fibers was managed by an incremental approach to enhancing our established algorithm for decomposing sEMG signals obtained from isometric contractions. We used machine-learning algorithms and time-varying MUAP shape discrimination to decompose the sEMG signal from an increasingly challenging sequence of pseudostatic and dynamic contractions. The accuracy of the decomposition results was assessed by two verification methods that have been independently evaluated. The firing instances of the motor units had an accuracy of ∼90% with a MUAP train yield as high as 25. Preliminary observations from the performance of motor units during cyclic contractions indicate that during repetitive dynamic contractions, the control of motor units is governed by the same rules as those evidenced during isometric contractions. Modifications in the control properties of motoneuron firings reported by previous studies were not confirmed. Instead, our data demonstrate that the common drive and hierarchical recruitment of motor units are preserved during concentric and eccentric contractions.

High-yield decomposition of surface EMG signals.

OBJECTIVE: Automatic decomposition of surface electromyographic (sEMG) signals into their constituent motor unit action potential trains (MUAPTs). METHODS: A small five-pin sensor provides four channels of sEMG signals that are in turn processed by an enhanced artificial intelligence algorithm evolved from a previous proof-of-principle. We tested the technology on sEMG signals from five muscles contracting isometrically at force levels ranging up to 100% of their maximal level, including those that were covered with more than 1.5cm of adipose tissue. Decomposition accuracy was measured by a new method wherein a signal is first decomposed and then reconstructed and the accuracy is measured by comparison. Results were confirmed by the more established two-source method. RESULTS: The number of MUAPTs decomposed varied among muscles and force levels and mostly ranged from 20 to 30, and occasionally up to 40. The accuracy of all the firings of the MUAPTs was on average 92.5%, at times reaching 97%. CONCLUSIONS: Reported technology can reliably perform high-yield decomposition of sEMG signals for isometric contractions up to maximal force levels. SIGNIFICANCE: The small sensor size and the high yield and accuracy of the decomposition should render this technology useful for motor control studies and clinical investigations.

Surface EMG signal decomposition using empirically sustainable biosignal separation principles.

We introduce the concept of empirically sustainable principles for biosignal separation as a means of addressing the complexities that are practically encountered in the decomposition of surface electromyographic (sEMG) signals. Recently, we have identified two new principles of this type. The first principle places upper bounds on the inter-firing intervals and residual signal energies of the separated components. The second principle seeks a local minimum in the coefficient of variation of inter-firing intervals of each separated component. Upon incorporation of these principles into our latest Precision Decomposition system for sEMG signals, 20 to 30 motor unit action potential trains (MUAPTs) were decomposed per experimental sEMG signal from isometric contractions with trapezoidal force profiles. Our new system performs well even as the force generated by a muscle approaches maximum voluntary levels.

A combined sEMG and accelerometer system for monitoring functional activity in stroke.

Remote monitoring of physical activity using body-worn sensors provides an alternative to assessment of functional independence by subjective, paper-based questionnaires. This study investigated the classification accuracy of a combined surface electromyographic (sEMG) and accelerometer (ACC) sensor system for monitoring activities of daily living in patients with stroke. sEMG and ACC data (eight channels each) were recorded from 10 hemiparetic patients while they carried out a sequence of 11 activities of daily living (identification tasks), and 10 activities used to evaluate misclassification errors (nonidentification tasks). The sEMG and ACC sensor data were analyzed using a multilayered neural network and an adaptive neuro-fuzzy inference system to identify the minimal sensor configuration needed to accurately classify the identification tasks, with a minimal number of misclassifications from the nonidentification tasks. The results demonstrated that the highest sensitivity and specificity for the identification tasks was achieved using a subset of four ACC sensors and adjacent sEMG sensors located on both upper arms, one forearm, and one thigh, respectively. This configuration resulted in a mean sensitivity of 95.0%, and a mean specificity of 99.7% for the identification tasks, and a mean misclassification error of < 10% for the nonidentification tasks. The findings support the feasibility of a hybrid sEMG and ACC wearable sensor system for automatic recognition of motor tasks used to assess functional independence in patients with stroke.

Aliasing rejection in Precision Decomposition of EMG signals.

The use of Artificial Intelligence (AI) methods in Precision Decomposition (PD) of indwelling and surface electromyographic (EMG) signals has led to the recent development of systems that can automatically resolve most instances of complex superposition among action potentials. The remaining errors have to be corrected by a user-interactive editing process. Typically, 25% to 50% of such errors involve action-potential aliasing, whereby the action potential of a motor unit is incorrectly identified in signal data that actually supports the action potential of another motor unit. To drastically reduce this class of errors, we have added a new aliasing-rejection mechanism in PD algorithms. Experimental results on real EMG signals show that aliasing-related errors of the Precision Decomposition technique are thereby reduced by 80% to 90%.