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

Abstract

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 24 hours 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 24-hours. Two-channels of thenar muscle surface EMG were recorded over 24-hours from 7 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-hour 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 7 24-hour 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 24 hours 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. Correspondence to Jeffrey Winslow, jwinslow@cableone.net. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. NIH Public Access NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript IntroductionSpinal cord injured (SCI) individuals often have no voluntary control over skeletal muscles that are innervated from spinal segments below the lesion. However, these paralyzed muscles can contract involuntarily (spasm) in response to various stimuli such as vibration, cutaneous inputs, and changes in temperature and body position (Kawamura et al., 1989;Little et al., 1989;Thomas and Ross, 1997). In other situations, weak involuntary contractions have been recorded for up to 30 minutes, particularly in hand muscles Zijdewind and Thomas 2001; Zijdewind et al. 2004). Whether this spontaneous motor unit activity takes place over extended time periods is unknown. This activity appears to be spontaneous in that no obvious stimulus generates the motor unit activity. These different types of muscle activity can be...

Spike sorting paradigm for classification of multi-channel recorded fasciculation potentials

“…However in this earlier work, only one set of data was analyzed using a previously developed EMG decomposition technique, [22]. Another study in FP classification was performed by Winslow et al [36], This focused on classification of 24hour-long EMG (at rest) from the paralyzed hand muscles of subjects with spinal cord injury. However, their technique was designed for only two-channel EMG data.…”

Automatic Identification and Classification of Muscle Spasms in Long-Term EMG Recordings

“…Each experiment consisted of 192 h of data per subject (8 channels × 24 h). EMG recording sites were as described by Mummidisetty et al [16]. Briefly, self-adhering electrodes (Superior Silver, no.…”

“…4) instead of just relying on spectrum peak ratios. Additionally, wavelet transforms could be used to analyze the EMG envelope signal because they provide better time resolution than a short-time Fourier transform [16], [17]. Even though clonus had been previously analyzed using wavelets, it was not automatically classified as accomplished by the current algorithm.…”

“…Even though clonus had been previously analyzed using wavelets, it was not automatically classified as accomplished by the current algorithm. Mummidisetty et al [16] identified EMG burst start and end times within already recognized clonus.…”

Електроміографічна Діагностика Порушень Моторики Язика Неврологічного Генезу У Пацієнтів З Оклюзійною Патологією

“…Пошире-ним методом діагностики розладів функціонування м'язів є поверхнева електроміографія [15][16][17][18]. Най-більш вивченою у стоматології є методика електро-міографії жувальних м'язів.…”