Intervening Leg Movements Disrupt PLMS Sequences.

Study Objectives: Periodic leg movements during sleep (PLMS) occur within a subject as a series with a remarkably stable period defined by the intermovement interval (IMI). Sometimes a non-PLMS movement occurs intervening between two PLMS. PLMS scoring rules totally ignore these intervening leg movements (iLM). This implicitly assumes an iLM results from a process sufficiently independent from the periodic process producing PLMS that it does not affect the periodicity of the surrounding PLMS. This study for the first time tests this basic assumption and explores characteristics of iLM as a potentially significant class of leg movements during sleep. Methods: Leg movements were analyzed from two nights of polysomnography recordings from 27 RLS patients and 22 controls using the validated MATPLM1.1 program. All periods (IMI) between PLMS containing an iLM were compared to the local PLMS period defined as the immediately preceding PLMS IMI using pairwise two-sided Wilcoxon sign-rank tests. Similarly, iLM were tested to see if they started a new PLMS series by having the same period as the subsequent PLMS. Results: The periods (IMIs) containing iLM were longer than the previous periods in RLS subjects, but not controls (p < .05). The periods beginning with the iLM were shorter than the subsequent periods in both RLS and controls (p < .05). Conclusions: iLM as a separate type of LM distort PLMS periodicity and do not restart PLMS series. iLM end PLMS series.

Defining morphology of periodic leg movements in sleep: an evidence-based definition of a minimum window of sustained activity.

OBJECTIVE: Current standard guidelines for scoring periodic leg movements (PLM) define the start and end of a movement but fail to explicitly specify the movement morphology necessary to classify an EMG event as a PLM, rather than some other muscle event. This is currently left to the expert visual scorer to determine. This study aimed to define this morphology to provide a consistent standard for visual scoring and to improve automatic periodic leg movements in sleep scoring. METHODS: A review of expert PLM scoring produced a hypothesized morphology criterion: a window of high EMG activity within the movement lasting at least 0.5 s. Two diverse expert visual scorers were independently presented with images of EMG tracings from candidate leg movements (CLM) that either passed or failed this requirement (aka "full" or "empty" movements, respectively), and indicated whether each should be scored as CLM. The 0.5-s window was compared with alternatives of 0.25 and 0.75 windows. RESULTS: Expert scorers on average identified 94 % of "full" movements as CLM in contrast to only 8.5 % of "empty" movements. The proposed minimum window of 0.5 s also resulted in the highest agreement between visual scorers and between scorers and an automatic program. CONCLUSION: An added criterion requiring 0.5 s of high EMG activity within a valid CLM improves the accuracy of automatic scoring algorithms in relation to the gold standard of expert visual scorers. Our results suggest that this rule is an accurate representation of the morphology feature used by experts. This new rule has the potential to improve consistency and accuracy of visual and automatic scoring of PLM.

Inter-movement interval as a primary stable measure of periodic limb movements of sleep.

AIM: Periodic leg movements in sleep (PLMS) are generally evaluated by the number of events per hour during sleep, but this is an unstable measure with marked nightly variability and also fails to assess the basic periodicity that essentially characterizes these movements. The inter-movement interval (IMI) evaluates a putative biological process producing the period of PLMS. By contrast, the actual number of PLMS reflects the expression of this biological process that would likely be affected by multiple factors, particularly those disrupting sleep. Thus, this study tests the hypothesis that measurement of IMI duration should be more stable over nights in comparison to any measure based on counting the number of movements. IMI approximates a log-normal distribution. This study, therefore, tests the hypothesis that the means of the log IMI are more stable from night 1 (N1) to night 2 (N2) sleep recordings compared with measures of the number of PLMS. METHODS: PLMS/h and IMI were measured for two consecutive nights of full sleep recordings for 29 restless legs syndrome (RLS) patients not being treated and 22 healthy controls without RLS. N1-N2 difference between nights was measured as percent of average for the nights. RESULTS: Mean log IMI showed little nightly variability (mean ± SD for RLS: 3.6% ± 3.7, controls: 7.1% ± 7.0) significantly less p < 0.001) in comparison to PLMS/h (mean ± SD for RLS: 43.2 ± 37.1, controls: 63.7 ± 40.8). The IMI nightly variability was also significantly better than that for the periodicity index. IMI also varied considerably between individuals. CONCLUSION: Mean log IMI is a remarkably stable measure across nights within a subject and shows differences between subjects that may have clinical and biological significance. Because of this consistency, the mean log IMI should be considered as one standard measure of PLMS alongside the PLMS index.

MATPLM1, A MATLAB script for scoring of periodic limb movements: preliminary validation with visual scoring.

BACKGROUND AND PURPOSE: A Matrix Laboratory (MATLAB) script (MATPLM1) was developed to rigorously apply World Associations of Sleep Medicine (WASM) scoring criteria for periodic limb movements in sleep (PLMS) from bilateral electromyographic (EMG) leg recordings. This study compares MATPLM1 with both standard technician and expert detailed visual PLMS scoring. METHODS AND SUBJECTS: Validation was based on a 'macro' level by agreement for PLMS/h during a night recording and on a 'micro' level by agreement for the detection of each PLMS from a stratified random sample for each subject. Data available for these analyses were from 15 restless leg syndrome (RLS) (age: 61.5 ± 8.5, 60% female) and nine control subjects (age: 61.4 ± 7.1, 67% female) participating in another study. RESULTS: In the 'micro' analysis, MATPLM1 and the visual detection of PLMS events agreed 87.7% for technician scoring and 94.4% for expert scoring. The technician and MATPLM1 scoring disagreements were checked for 36 randomly selected events, 97% involved clear technician-scoring error. In the 'macro' analysis, MATPLM1 rates of PMLS/h correlated highly with visual scoring by the technician (r(2) = 0.97) and the expert scorer (r(2) = 0.99), but the technician scoring was consistently less than MATPLM1: median (quartiles) difference: 10 (5, 23). There was little disagreement with expert scorer [median (quartile) difference: -0.3 (-2.4, 0.3)]. CONCLUSIONS: The MATPLM1 produces reliable scoring of PLMS that matches expert scoring. The standard visual scoring without careful measuring of events tends to significantly underscore PLMS. These preliminary results support the use of MATPLM1 as a preferred method of scoring PLMS for EMG recordings that are of a good quality and without significant sleep-disordered breathing events.