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

Abstract

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.

An On‐Skin Electrode with Anti‐Epidermal‐Surface‐Lipid Function Based on a Zwitterionic Polymer Brush

“…[ 10–13 ] In many occasions, the electrophysiological testing is required to be conducted for a long period of time ranging from hours to days to collect enough information. [ 14–17 ] Therefore, it is very important to develop on‐skin electrodes that can provide long‐term and home‐based recording of electrophysiology with high fidelity. The prevalent gelled electrodes show a significant drawback of signal degradation due to the drying out of the gel and the resultant increase in contact impedance.…”

Spasms after spinal cord injury show low-frequency intermuscular coherence

“…This report uses EMG data collected as part of a previous study that developed methods for detection and classification of spasms ( Winslow et al 2015 ). Seven spinal cord injury volunteers, 28 to 66 yr old (5 men, 2 women) took part in the study.…”

“…In this way, we were able to record involuntary spasms that occurred while subjects went about their normal daily life activities. We first used the previously reported algorithm to mark periods in each muscle where tonic spasms occurred; full details are given in Winslow et al (2015) . Briefly, the algorithm calculated the integral of the rectified EMG in 10-ms windows and compared this with a threshold level computed from a baseline region without muscle activity.…”

“…1 . Further details about the recording device and algorithm for classification of tonic spasms are described by Winslow et al (2015) .…”

“…After spinal cord injury, patients commonly experience involuntary muscle spasms; these can affect patient’s lives to varying degrees ( Thomas et al 2014b ). Spasms can be classified into different types, including tonic EMG activity, clonus, and unit firing ( Winslow et al 2015 ). After spinal cord injury, the contribution of afferent input to motoneuron output may increase: in some cases, a spasm can easily be evoked by stimuli such as light touch and small postural adjustments.…”