A critical review by
De Luca CJ and Kline JC
Technology for decomposing the EMG signal into individual motor unit action potentials (MUAPs) was introduced by us three decades ago (LeFever and De Luca, 1982). The Delsys dEMG decomposition system is the fruition of three decades of research and development. It substantially outperforms other decomposition systems. It consists of:
Decomposition Sensor Technologies
Advantages of the Delsys Small dEMG Sensor
Our radically new non-invasive sensor is specially designed (De Luca et al, 2006) to acquire sEMG signals that can be decomposed into MUAPs by our dEMG algorithm.
The geometry and the dimensions of the dEMG sensor are specifically selected to detect four channels of differential sEMG signals of a quality that renders them particularly useful for the pragmatic execution of the dEMG algorithm. Its effectiveness is supported by several advantages:
- A simple, 5 x 5 mm, five-pin geometry ensures that the sensor is easy to apply and maintains long-lasting electrode contact throughout an experiment.
- The small-footprint is versatile for both small and large muscles.
- It acquires sEMG signals rich in motor unit action potential trains (MUAPTs).
- Its design is fine-tuned to detect distinct uncontaminated MUAPs embedded within sEMG signals essential for the proper working of the dEMG algorithm.
Advantage of the Delsys dEMG algorithm
- Recover MUAP shapes and firing instances.
- Yield as many as 50 MUAPTs (typically 20-30) active during contractions ranging from 5 to 80% of the maximal voluntary contraction.
- Accuracy verified for each extracted MUAPT – on average 95%.
Our dEMG algorithm for decomposing sEMG signals is described in peer-reviewed publications by De Luca et al (2006) and Nawab et al (2010). It is an evolution of the algorithm first reported by LeFever and De Luca (1982). The dEMG algorithm is set apart from other approaches in the field because:
- It is grounded in fundamental principles of signal processing that are applied with no compromise.
- It uses template matching to track the changing shapes of MUAPs as they occur throughout the sEMG signal.
- It is agnostic to the underlying motor unit firing behavior.
Decomposition Error Reduction
Advantage of the Delsys Decomposition Error Reduction Algorithm
All decomposition algorithms are subject to imperfection; even those relying on visual identification of individual MUAPs by a human operator. We have developed a new process by which these decomposition errors can be identified and mitigated to improve the decomposition result (Kline and De Luca, 2014). The basic concept of the error-reduction algorithm consists of decomposing the recorded sEMG signal into multiple estimates of the constituent MUAPTs and combining these estimates to derive a new estimate with fewer and smaller decomposition errors.
Verification of Decomposition Accuracy
Advantage of the Delsys Verification of Decomposition Accuracy
Our Decompose, Synthesize, Decompose, Compare (DSDC) validation (Nawab et al, 2010; De Luca and Contessa, 2012) overcomes the shortcomings of other tests. It uses a physiologically realistic signal synthesized from MUAPTs obtained from the decomposition of a real sEMG signal. By adding Gaussian noise to the synthesized signal we create a unique signal that is of the same class as the real sEMG signal. By decomposing the synthesized signal and comparing the extracted MUAPTs with those known within the synthesized signal we are able to obtain the accuracy and location error of decomposition for each MUAPT. The efficacy of our DSDC validation is supported by several advantages:
- It validates the accuracy of each of the MUAPTs extracted from the sEMG signal.
- It makes no assumption about the characteristics of the MUAP shapes or variation of these shapes throughout the sEMG signal.
- It is agnostic to the underlying firing behavior of individual motor units.
Contemporary Discourse in the Literature
In a recent letter to the editor of the Journal of Applied Physiology (De Luca et al, 2015) we challenged the validation recommendations proposed by Farina, Merletti and Enoka (2014) and gave the authors an opportunity to respond to the following concerns. They provided no responses.
|Point 1||Previously Farina and Enoka (2011) claimed that decomposition of synthesized mathematically-generated signals is the best way to validate sEMG decomposition algorithms. But this approach does not test the accuracy of decomposing a real sEMG signal. As examples consider models developed by Farina and Merletti (2001) that ignore known physiological characteristics of MUAP shapes, firing instances or dependent firing behavior such as synchronization and common drive. Yet, these models have been used as the primary validation of the CKC decomposition algorithm. In a previous letter we (De Luca and Nawab, 2011) pointed out these limitations. Now Farina, Merletti and Enoka (2014) have reversed their position and admit that synthesized mathematically generated signals are “more limited than an experimental validation”. Their reversal was made clear in our letter (De Luca et al, 2015).|
|Point 2||As an alternative validation Farina, Merletti and Enoka (2014) now propose the “only current reliable approach to assess the accuracy of a surface EMG decomposition algorithm” is the two-source test. As discussed above, this test was developed and first used by us more than 4 decades ago. Being well aware of its strengths and limitations we expressed the primary drawback of the two-source test in our letter (De Luca et al, 2015).|
|Point 3||When Holobar et al (2014) used the two-source test to validate the CKC decomposition algorithm, they could only validate on average 0.7 MUAPTs from each contraction and reported some accuracy values less than 60%. How such low accuracy values from such a small percentage of MUAPTs establishes the validity of the CKC algorithm for sEMG decomposition remains confounding.|
|Point 4||To avoid the drawbacks of these other approaches we developed the decompose-synthesize-decompose-compare (DSDC) method to validate our dEMG algorithm. Farina, Merletti and Enoka (2014) claimed that our method is flawed and does not provide a reliable assessment of decomposition accuracy. We pointed out that when the DSDC validation was compared with the fraction of MUAPTs obtained using their favored two-source test, both validations yielded 95% average accuracy for our dEMG algorithm across different subjects, muscles and force levels. Only the DSDC validation had the advantage of evaluating the accuracy of the comprehensive set of MUAPTs, far surpassing the select few assessed by the two-source test.|
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