A reliable automatic algorithm to score fragmentary myoclonus

Abstract

SummaryExcessive fragmentary myoclonus (EFM) is an incidental polysomnographic finding requiring documentation of ≥20 minutes of NREM sleep with ≥5 fragmentary myoclonus (FM) potentials per minute. Manual FM scoring is time‐consuming and prone to inter‐rater variability. This work aimed to validate an automatic algorithm to score FM in whole‐night recordings. One expert scorer manually scored FM in the anterior tibialis muscles in 10 polysomnographies of as many subjects. The algorithm consisted of two steps. First, parameters of the automatic leg movement identification algorithm of the BrainRT software (OSG, Belgium) were modified to identify FM‐like activity. Second, a post‐processing algorithm was implemented to remove FM activity not meeting sufficient amplitude criteria. The parameter choice and the post‐processing were optimised with leave‐one‐out cross‐validation. Agreement with the human scorer was measured with Cohen's kappa (k) and correlation between manual and automatic FM indices in different sleep stages was evaluated. Agreement in identifying patients with EFM was computed. The algorithm showed substantial agreement (average k > 0.62) for all sleep stages, except for W, where a moderate agreement was observed (average k = 0.58). Nonetheless, the agreement between human scorer and the algorithm was similar to previously reported values of inter‐rater variability for FM scoring. Correlation coefficients were over 0.96 for all sleep stages. Furthermore, the presence/absence of EFM was correctly identified in 80% of the subjects. In conclusion, this work presents a reliable algorithm for automatic scoring of FM and EFM. Future studies will apply it to objectively and consistently evaluate FM indices and the presence of EFM in large populations.

A 24% prevalence of excessive fragmentary myoclonus in 500 consecutive sleep laboratory patients

“…A previously validated algorithm developed by our group (Bergmann, Hogl, et al, 2023) was applied to all VPSGs included in the study. The algorithm identified FM in AT channels according to the AASM definition (Troester et al, 2023) and returned as an output for each patient the FMI in TST, wakefulness (W; from lights off to lights on), N1, N2, N3, and REM sleep.…”