Automatic analysis of EMG during clonus

Abstract

Clonus can disrupt daily activities after spinal cord injury. Here an algorithm was developed to automatically detect contractions during clonus in 24h 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.

Fig. 1

EMG during clonus. A. Three cycles of clonus with the start (---) and end (…) of the unrectified EMG marked for each contraction. The EMG were recorded from the left medial gastrocnemius muscle (hour 15: 3–4 pm) of a subject with a SCI at C4. B. The same EMG rectified and with an overlay of the linear envelopes generated for the high, intermediate and low frequency bands. Arrows show the EMG peaks identified by the algorithm without constraints. C. The EMG from one contraction enclosed by a default window (---), a resized window (…), and the start and end of the EMG (^__) determined by the algorithm for the resized window. Shortening the window excluded the motor unit potential between the contractions.

Fig. 2

Flow chart of the steps the algorithm implemented to automatically identify contractions during clonus and to mark the starts and ends of the EMG. EMG (globally processed) was input to the algorithm twice.

Fig. 3

Algorithm performance with and without constraints. A. Median number of contractions identified by the algorithm with no constraints, one, or two sets of constraints (filled bars) compared to the analysis of Person 1 (n=31 spasms, n=7 subjects, 5 spasms per subject except when clonus occurred once in a 24 hour recording). B. Same data as in A, expressed as a percentage of the results generated by Person 1. C. Median (25th and 75th percentiles) agreement for common contractions measured by Person 1 (P1) versus Person 2 (P2) compared to Person 1 versus the Program (Pr). Data for each subject are shown with different symbols.

Fig. 4

Agreement on clonus frequency, duration and intensity. The median (25th and 75th percentiles) agreement for clonus frequency (A), EMG duration (B), and RMS EMG (C) analyzed by Person 1 (P1) and Person 2 (P2) versus Person 1 and the Program (Pr, n=7 subjects). Results for each subject are shown by unique symbols.