Automatic analysis of EMG during clonus

Clonus can disrupt daily activities after spinal cord injury. Here an algorithm was developed to automatically detect contractions during clonus in 24 h electromyographic (EMG) records. Filters were created by non-linearly scaling a Mother (Morlet) wavelet to envelope the EMG using different frequency bands. The envelope for the intermediate band followed the EMG best (74.8–193.9 Hz). Threshold and time constraints were used to reduce the envelope peaks to one per contraction. Energy in the EMG was measured 50 ms either side of each envelope (contraction) peak. Energy values at 5% and 95% maximal defined EMG start and end time, respectively. The algorithm was as good as a person at identifying contractions during clonus (p = 0.946, n = 31 spasms, 7 subjects with cervical spinal cord injury), and marking start and end times to determine clonus frequency (intra class correlation coefficient, α: 0.949), contraction intensity using root mean square EMG (α: 0.997) and EMG duration (α: 0.852). On average the algorithm was 574 times faster than manual analysis performed independently by two people (p ≤ 0.001). This algorithm is an important tool for characterization of clonus in long-term EMG records.

► Novel algorithm to automatically characterize clonus in long-term EMG records.
► Wavelets were scaled non-linearly to extract time–frequency information from EMG.
► Threshold and time constraints improved algorithm accuracy.
► Robust algorithm performance across muscles, subjects, and time.
► Potential research tool to facilitate analysis of involuntary muscle contractions.