Improved motor unit number estimate when motor unit alternation is addressed.

Motor unit number estimation (MUNE) is important for determining motoneuron survival with age or in conditions such as amyotrophic lateral sclerosis or spinal cord injury. The original incremental method and approaches that were introduced to minimize alternation (e.g., multiple-point stimulation) are most commonly used, but one must accept the limitation that alternation of motor units may still inflate the estimate. Alternation occurs because axon thresholds are probabilistic and overlap for different axons; therefore, different combination of motor units may respond at a given stimulus intensity. Our aims were to quantify motor unit alternation systematically in the thenar muscles of 35 healthy adults by digital subtraction of EMG and force, and to compare MUNE with and without alternation. Alternation was prevalent, with one to nine occurrences in the first 7 to 11 steps in EMG in 34 of 35 muscles. It occurred in the first 3 steps in EMG in 49% of muscles. This alternation resulted in fewer units than steps in EMG (3 to 10 units at step 7 to 11). Accounting for alternation using digital subtraction reduced MUNE by up to 50%, day-to-day, and between-participant variability in MUNE. These results highlight the need to quantify alternation to improve the reliability and precision of motor unit number estimates, which will allow for detection of smaller changes in motoneuron survival with age, various health conditions, and/or due to an intervention. NEW & NOTEWORTHY Motor unit alternation was quantified systematically for the first time, addressing a major limitation of motor unit number estimates. Accounting for alternation decreased motor unit number estimates, and improved the reliability and precision of the motor unit number estimate, which will allow smaller, clinically relevant changes in motoneuron survival to be detected.

Automatic identification and classification of muscle spasms in long-term EMG recordings.

Spinal cord injured (SCI) individuals may be afflicted by spasticity, a condition in which involuntary muscle spasms are common. EMG recordings can be analyzed to quantify this symptom of spasticity but manual identification and classification of spasms are time consuming. Here, an algorithm was created to find and classify spasm events automatically within 24-h recordings of EMG. The algorithm used expert rules and time-frequency techniques to classify spasm events as tonic, unit, or clonus spasms. A companion graphical user interface (GUI) program was also built to verify and correct the results of the automatic algorithm or manually defined events. Eight channel EMG recordings were made from seven different SCI subjects. The algorithm was able to correctly identify an average (±SD) of 94.5 ± 3.6% spasm events and correctly classify 91.6 ± 1.9% of spasm events, with an accuracy of 61.7 ± 16.2%. The accuracy improved to 85.5 ± 5.9% and the false positive rate decreased to 7.1 ± 7.3%, respectively, if noise events between spasms were removed. On average, the algorithm was more than 11 times faster than manual analysis. Use of both the algorithm and the GUI program provide a powerful tool for characterizing muscle spasms in 24-h EMG recordings, information which is important for clinical management of spasticity.

Fatigue compensation during FES using surface EMG.

Muscle fatigue limits the effectiveness of FES when applied to regain functional movements in spinal cord injured (SCI) individuals. The stimulation intensity must be manually increased to provide more force output to compensate for the decreasing muscle force due to fatigue. An artificial neural network (ANN) system was designed to compensate for muscle fatigue during functional electrical stimulation (FES) by maintaining a constant joint angle. Surface electromyography signals (EMG) from electrically stimulated muscles were used to determine when to increase the stimulation intensity when the muscle's output started to drop. In two separate experiments on able-bodied subjects seated in hard back chairs, electrical stimulation was continuously applied to fatigue either the biceps (during elbow flexion) or the quadriceps muscle (during leg extension) while recording the surface EMG. An ANN system was created using processed surface EMG as the input, and a discrete fatigue compensation control signal, indicating when to increase the stimulation current, as the output. In order to provide training examples and test the systems' performance, the stimulation current amplitude was manually increased to maintain constant joint angles. Manual stimulation amplitude increases were required upon observing a significant decrease in the joint angle. The goal of the ANN system was to generate fatigue compensation control signals in an attempt to maintain a constant joint angle. On average, the systems could correctly predict 78.5% of the instances at which a stimulation increase was required to maintain the joint angle. The performance of these ANN systems demonstrates the feasibility of using surface EMG feedback in an FES control system.

Automatic classification of motor unit potentials in surface EMG recorded from thenar muscles paralyzed by spinal cord injury.

Involuntary electromyographic (EMG) activity has only been analyzed in the paralyzed thenar muscles of spinal cord injured (SCI) subjects for several minutes. It is unknown if this motor unit activity is ongoing. Longer duration EMG recordings can investigate the biological significance of this activity. Since no software is currently capable of classifying 24h of EMG data at a single motor unit level, the goal of this research was to devise an algorithm that would automatically classify motor unit potentials by tracking the firing behavior of motor units over 24h. Two channels of thenar muscle surface EMG were recorded over 24h from seven SCI subjects with a chronic cervical level injury using a custom data logging device with custom software. The automatic motor unit classification algorithm developed here employed multiple passes through these 24-h EMG recordings to segment, cluster, form global templates and classify motor unit potentials, including superimposed potentials. The classification algorithm was able to track an average of 19 global classes in seven 24-h recordings with a mean (+/-SE) accuracy of 89.9% (+/-0.98%) and classify potentials from these individual motor units with a mean accuracy of 90.3% (+/-0.97%). The algorithm could analyze 24h of data in 2-3 weeks with minimal input from a person, while a human operator was estimated to take more than 2 years. This automatic method could be applied clinically to investigate the fasciculation potentials often found in motoneuron disorders such as amyotrophic lateral sclerosis.